1. Bibliographic Information

1.1. Title

The central topic of the paper is "Difix3D+: Improving 3D Reconstructions with Single-Step Diffusion Models." This title indicates the paper's focus on enhancing 3D reconstruction and novel-view synthesis quality by leveraging efficient, single-step diffusion models.

1.2. Authors

The authors are: Jay Zhangjie Wu, Yuxuan Zhang, Haithem Turki, Xuanchi Ren, Jun Gao, Mike Zheng Shou, Sanja Fidler, Zan Gojcic, and Huan Ling. Their affiliations include NVIDIA, National University of Singapore, University of Toronto, and Vector Institute. These affiliations suggest a strong background in computer vision, machine learning, and potentially graphics research, given NVIDIA's prominence in GPU-accelerated computing and AI.

1.3. Journal/Conference

The paper was published on arXiv. The abstract was published on "2025-03-03T17:58:33.000Z," indicating it is a recent preprint. As an arXiv preprint, it has not yet undergone formal peer review by a journal or conference, but arXiv serves as a widely recognized platform for disseminating early research in AI and computer science.

1.4. Publication Year

The paper was published as a preprint in 2025.

1.5. Abstract

The paper introduces $Difix3D+$, a novel pipeline aimed at enhancing 3D reconstruction and novel-view synthesis, particularly in addressing photorealistic rendering challenges from extreme viewpoints where artifacts commonly persist. The core of their approach is Difix, a single-step image diffusion model specifically trained to identify and remove artifacts in rendered novel views, especially those arising from underconstrained regions of the 3D representation. Difix serves two primary functions:

1. During Reconstruction: It cleans up pseudo-training views (views rendered from the reconstruction) before they are distilled back into the 3D representation. This process significantly improves underconstrained regions and the overall quality of the 3D model.
2. During Inference: It acts as a neural enhancer for real-time post-processing, effectively removing residual artifacts that stem from imperfect 3D supervision or limitations of current reconstruction models. $Difix3D+$ is presented as a general and compatible solution for both Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) representations. The method achieves an average 2x improvement in FID score over baselines while maintaining 3D consistency.

1.6. Original Source Link

Official Source: https://arxiv.org/abs/2503.01774 PDF Link: https://arxiv.org/pdf/2503.01774v1.pdf Publication Status: This paper is an arXiv preprint, meaning it has been publicly shared but has not yet undergone formal peer review for publication in a conference or journal.

2. Executive Summary

2.1. Background & Motivation

The core problem the paper aims to solve is the persistent challenge of achieving photorealistic rendering from extreme novel viewpoints in 3D reconstruction and novel-view synthesis. While recent advancements like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have revolutionized the field, they still struggle with artifacts such as spurious geometry, blurriness, and missing regions, particularly in underconstrained areas (regions not well-covered by input training views) or when generating views significantly different from the training data.

This problem is important because these artifacts hamper the suitability of 3D reconstruction methods for real-world applications, especially in domains requiring high visual fidelity and robustness across various viewing angles, such as autonomous driving or virtual reality. Existing methods often rely on per-scene optimization, which is susceptible to shape-radiance ambiguity (where a 3D representation can perfectly reproduce training images without accurately reflecting the true scene geometry) and lacks the ability to hallucinate plausible geometry in underconstrained regions without strong data priors.

The paper identifies a gap: large 2D generative models like diffusion models have learned powerful priors from vast internet-scale datasets, generalizing well across many scenes. However, efficiently and effectively lifting these 2D priors to 3D, especially for large scenes and without time-consuming per-training-step queries to the diffusion model, remains an open challenge.

The paper's entry point is to leverage the speed and visual knowledge of single-step diffusion models to "fix" rendering artifacts. By efficiently adapting these models to enhance rendered views, and then distilling these improvements back into the 3D representation, the authors aim to overcome the limitations of current 3D reconstruction methods in handling underconstrained regions and extreme novel views.

2.2. Main Contributions / Findings

The paper makes the following primary contributions:

1. Efficient Adaptation of 2D Diffusion Models for 3D Artifact Removal: The authors demonstrate how to adapt 2D diffusion models to remove artifacts from NeRF/3DGS renderings with minimal effort. The fine-tuning process for their DiFIx model is highly efficient, taking only a few hours on a single consumer graphics card. Crucially, a single DiFIx model is shown to be powerful enough to handle artifacts from both implicit (NeRF) and explicit (3DGS) representations.

2. Progressive 3D Update Pipeline for Multi-View Consistency: The paper proposes an innovative progressive 3D update pipeline. This pipeline refines the 3D representation by distilling the improved novel views (generated by DiFIx) back into the 3D model. This iterative process ensures multi-view consistency and significantly enhances the quality of the 3D representation, especially in underconstrained regions. This approach is notably efficient, being more than 10 times faster than contemporary methods that query a diffusion model at each training step.

3. Real-Time Post-Processing with Single-Step Diffusion: The work highlights how single-step diffusion models enable near real-time post-processing. DiFIx is applied directly to the outputs of the improved 3D reconstruction as a final enhancement step during inference, further boosting novel view synthesis quality without significant latency. This real-time post-rendering step contributes to higher perceptual quality and sharpness.

   The key findings are that $Difix3D+$ is a general solution compatible with both NeRF and 3DGS representations, achieving significant quantitative improvements. Specifically, it attains an average 2x improvement in FID score over baselines, alongside gains in PSNR, SSIM, and reductions in LPIPS, while effectively maintaining 3D consistency. This solves the problem of persistent artifacts in extreme novel views and underconstrained regions, making 3D reconstructions more photorealistic and robust for real-world scenarios.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand $Difix3D+$, a foundational understanding of Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Diffusion Models (DMs) is crucial.

3.1.1. Neural Radiance Fields (NeRF)

Neural Radiance Fields (NeRF) [37] is a technique that represents a 3D scene as a continuous volumetric function, typically implemented by a multilayer perceptron (MLP). Instead of storing explicit geometry (like polygons), NeRF learns to predict the color and density of any point in 3D space from any viewpoint.

• Concept: Imagine a scene. For any given point (x, y, z) in that scene, and for any viewing direction $(\theta, \phi)$, a NeRF model learns to output two things:

  1. Color ($c$): An RGB color value indicating what color that point appears to be when viewed from that direction.
  2. Volume Density ($\sigma$): A scalar value indicating the probability of a ray terminating at that point. Higher density means the point is more opaque.

• Training: NeRF is trained by taking a set of 2D images of a scene from different known camera poses. For each pixel in a training image, a ray is cast from the camera through that pixel into the 3D scene. Points are sampled along this ray, and for each sampled point, the MLP predicts its color and density. These predicted values are then volume rendered to produce a pixel color. The difference between this rendered color and the ground-truth pixel color is used to update the MLP's weights via gradient descent.

• Rendering: Once trained, to render a novel view (a view not seen during training), rays are cast from the desired novel camera pose through each pixel. The MLP predicts colors and densities along these rays, and volume rendering (an optical process that combines colors and opacities along a ray) is used to synthesize the final image.

  The paper utilizes the volume rendering formulation: $ \mathcal { C } ( \mathbf { p } ) = \sum _ { i = 1 } ^ { N } \alpha _ { i } \mathbf { c } _ { i } \prod _ { j } ^ { i - 1 } ( 1 - \alpha _ { j } ) $ Here:

• $\mathcal { C } ( \mathbf { p } )$ is the predicted color of a pixel (or ray).

• $N$ is the number of samples taken along a ray $\mathbf { r } ( \tau ) = o + t d$, where $o$ is the ray origin and $d$ is the ray direction.

• $\mathbf { c } _ { i }$ is the RGB color predicted by the MLP for the $i$-th sampled point on the ray.

• $\alpha _ { i }$ is the transmittance weight or alpha value for the $i$-th sample, calculated as $\alpha _ { i } ~ = ~ ( 1 - \exp ( - \sigma _ { i } \delta _ { i } ) )$.

  • $\sigma _ { i }$ is the volume density predicted by the MLP for the $i$-th sampled point.
  • $\delta _ { i }$ is the step size between sample points along the ray.

• $\prod _ { j } ^ { i - 1 } ( 1 - \alpha _ { j } )$ represents the transmittance (the probability that light reaches the $i$-th sample without being absorbed by previous samples). This term ensures that light is accumulated correctly from front to back.

3.1.2. 3D Gaussian Splatting (3DGS)

3D Gaussian Splatting [20] is a more recent and explicit 3D representation technique that offers real-time rendering capabilities while maintaining high quality.

• Concept: Instead of a continuous neural field, 3DGS represents a scene as a collection of numerous 3D Gaussians. Each Gaussian is a primitive defined by a set of parameters that describe its position, shape, orientation, opacity, and color.
• Parameters: Each Gaussian $i$ is parameterized by:
  • Position $\pmb { \mu } _ { i } \in \mathbb { R } ^ { 3 }$: The 3D center of the Gaussian.
  • Rotation $\mathbf { r } _ { i } \in \mathbb { R } ^ { 4 }$: A quaternion representing the orientation of the Gaussian.
  • Scale $\mathbf { s } _ { i } \in \mathbb { R } ^ { 3 }$: The extents of the Gaussian along its principal axes.
  • Opacity $\eta _ { i } \in \mathbb { R }$: How transparent or opaque the Gaussian is.
  • Color $\mathbf { c } _ { i } \in \mathbb { R } ^ { 3 }$: The RGB color of the Gaussian.
• Training: 3DGS is trained by optimizing these Gaussian parameters to reconstruct input 2D images. Stochastic gradient descent is used to adjust these parameters, often starting from an initial set of Gaussians derived from Structure-from-Motion (SfM) point clouds. Adaptive density control (adding or removing Gaussians) and anisotropic scaling (adjusting Gaussian shapes) are key optimization techniques.
• Rendering: Novel views are rendered by projecting the 3D Gaussians onto the 2D image plane. The alpha value for a Gaussian is computed based on its opacity and a 2D covariance matrix derived from its 3D rotation and scale, as seen from the camera viewpoint. These alpha values are then combined using the same volume rendering formulation as NeRF, but applied to the projected Gaussians in 2D.
• Gaussian alpha calculation: $ \alpha _ { i } = \eta _ { i } \exp \left[ - { \frac { 1 } { 2 } } \left( \mathbf { p } - { \pmb \mu } _ { i } \right) ^ { \top } \pmb { \Sigma } _ { i } ^ { - 1 } \left( \mathbf { p } - { \pmb \mu } _ { i } \right) \right] $ Here:
  • $\alpha _ { i }$ is the opacity contribution of the $i$-th Gaussian.
  • $\eta _ { i }$ is the base opacity of the $i$-th Gaussian.
  • $\mathbf { p }$ is a 3D point in space.
  • $\pmb { \mu } _ { i }$ is the center of the $i$-th Gaussian.
  • $\pmb { \Sigma } _ { i }$ is the 3D covariance matrix for the $i$-th Gaussian, which defines its shape and orientation. It is computed as $\pmb { \Sigma } = \pmb { R } \pmb { S } \pmb { S } ^ { T } \pmb { R } ^ { T }$.
    • $\pmb { R } \in \mathrm { SO } ( 3 )$ is the rotation matrix derived from the quaternion $\mathbf { r } _ { i }$.
    • $\pmb { S } \in \mathbb { R } ^ { 3 \times 3 }$ is a diagonal scaling matrix derived from the scale $\mathbf { s } _ { i }$.
  • The term $\left( \mathbf { p } - { \pmb \mu } _ { i } \right) ^ { \top } \pmb { \Sigma } _ { i } ^ { - 1 } \left( \mathbf { p } - { \pmb \mu } _ { i } \right)$ measures the squared Mahalanobis distance from point $\mathbf { p }$ to the center of the Gaussian, weighted by its covariance. This means points closer to the center of the Gaussian and aligned with its principal axes contribute more. The number $N$ of Gaussians that contribute to each pixel is determined through tile-based rasterization, an efficient parallel rendering technique.

3.1.3. Diffusion Models (DMs)

Diffusion Models (DMs) [16, 54, 57] are a class of generative models that have shown remarkable success in generating high-quality images. They work by learning to reverse a diffusion process that gradually adds noise to data.

• Concept:
  1. Forward Diffusion Process: In this process, a clean image $\mathbf { x }$ is progressively corrupted by adding Gaussian noise over several time steps $\tau$. After many steps, the image becomes pure Gaussian noise. The noisy image at time $\tau$ is denoted as $\mathbf { x } _ { \tau }$.
  2. Reverse Denoising Process: The diffusion model learns to reverse this process. It is trained to predict the noise that was added to $\mathbf { x } _ { \tau }$ to get to $\mathbf { x } _ { \tau - 1 }$ (or to predict the original clean image $\mathbf { x }$ directly). By iteratively removing the predicted noise, the model can transform pure Gaussian noise back into a coherent, high-quality image.
• Training Objective: DMs are trained using a denoising score matching objective [16, 18, 33, 54, 56, 57, 61]. The model $\mathbf { F } _ { \pmb { \theta } }$ (often a U-Net architecture) is trained to predict the noise $\epsilon$ added to the image. $ \begin{array} { r } { \mathbb { E } _ { \mathbf { x } \sim p _ { \mathrm { d a t a } } , \tau \sim p _ { \tau } , \epsilon \sim { \mathcal N } ( \mathbf { 0 } , I ) } \left[ \lVert \mathbf { y } - \mathbf { F } _ { \theta } ( \mathbf { x } _ { \tau } ; \mathbf { c } , \tau ) \rVert _ { 2 } ^ { 2 } \right] , } \end{array} $ Here:
  • $\mathbb { E } [ \cdot ]$ denotes the expectation.
  • $\mathbf { x } \sim p _ { \mathrm { d a t a } }$ means $x$ is sampled from the real data distribution.
  • $\tau \sim p _ { \tau }$ means the time step $\tau$ (noise level) is sampled from a distribution, often a uniform distribution $\mathcal { U } ( 0, 1000 )$.
  • $\epsilon \sim { \mathcal N } ( \mathbf { 0 } , I )$ means Gaussian noise epsilon is sampled from a standard normal distribution.
  • $\mathbf { x } _ { \tau } = \alpha _ { \tau } { \bf x } + \sigma _ { \tau } \epsilon$ is the noisy version of the input data $\mathbf { x }$ at time $\tau$. $\alpha _ { \tau }$ and $\sigma _ { \tau }$ are scheduling functions that control the noise level.
  • $\mathbf { F } _ { \theta } ( \mathbf { x } _ { \tau } ; \mathbf { c } , \tau )$ is the denoiser model with learnable parameters $\pmb \theta$, which takes the noisy image $\mathbf { x } _ { \tau }$, optional conditioning information $\mathbf { c }$ (e.g., text prompt), and time step $\tau$ as input.
  • $\mathbf { y }$ is the target vector, typically the noise $\epsilon$ itself, or the original clean image $\mathbf { x }$.
  • The loss function is the mean squared error (MSE) between the model's prediction and the target.

3.1.4. Single-Step Diffusion Models

Traditional DMs require many iterative denoising steps to generate a high-quality image, which can be computationally expensive and slow for inference. Single-step diffusion models (e.g., SD-Turbo [49], LCMs [32]) are designed to achieve high-quality generation in just one or a few steps. They typically achieve this by distilling knowledge from a multi-step DM into a faster model, allowing for much quicker inference while retaining much of the generative power. $Difix3D+$ leverages SD-Turbo for its efficiency.

3.2. Previous Works

The paper contextualizes its work within several streams of research:

3.2.1. Improving 3D Reconstruction Discrepancies

Many methods focus on making NeRF/3DGS robust to imperfections in input data.

• Camera Pose Optimization: Works like [6, 21, 35, 39, 59, 69] optimize camera poses alongside the 3D representation to correct for noisy camera inputs.
• Lighting Variations: Methods such as [34, 60, 73] address inconsistencies due to varying lighting conditions across images.
• Transient Occlusions: Approaches like [48] mitigate artifacts caused by transient objects or occlusions in the scene.
• Differentiation: While these methods improve robustness during training, they don't fully eliminate discrepancies. $Difix3D+$ addresses this by applying a fixer not only during reconstruction but also at render time as a post-processing step, directly improving quality in affected areas.

3.2.2. Priors for Novel View Synthesis

These works aim to improve rendering quality in under-observed scene regions.

• Geometric Priors:
  • Regularization [38, 55, 75]: Adding constraints to the optimization process to encourage smoother or more plausible geometry.
  • Pretrained Models [7, 45, 63, 90]: Using external models to provide supervision for depth or surface normals.
• Feedforward Neural Networks:
  • Enhancing Rendered Views [88]: Models that take a rendered view and enhance it using information from nearby reference views. NeRFLiX [88] is a prominent example.
  • Directly Predicting Novel Views [4, 31, 44, 79]: Models that directly synthesize a new view from multiple input views.
• Limitations & Differentiation: These deterministic methods can struggle with blurry results in ambiguous regions where multiple plausible renderings exist. They also tend to yield only marginal improvements in denser captures and are sensitive to noise. $Difix3D+$ uses a generative prior (diffusion model) which can hallucinate plausible details in ambiguous regions, and operates both during optimization and inference for comprehensive improvement.

3.2.3. Generative Priors for Novel View Synthesis

This category increasingly uses generative models (especially diffusion models) due to their strong priors learned from vast datasets.

• GAN-based Enhancements: GANeRF [46] trains a per-scene generative adversarial network (GAN) to enhance NeRF's realism.
• Diffusion Model Guidance (Expensive): Many works query diffusion models at each optimization step to guide the 3D representation [12, 25, 70, 72, 89]. These are often time-consuming and scale poorly to large environments. Nerfbusters [70] is an example that uses a 3D diffusion model for artifact removal.
• Diffusion Model Guidance (Pseudo-Observations): Deceptive-NeRF [27] and 3DGS-Enhancer [28] (concurrent work) use diffusion priors to enhance pseudo-observations rendered from the 3D representation, then augment the training set. This reduces the overhead by avoiding per-step querying.
• Differentiation of Difix3D+:
  • Efficiency: $Difix3D+$ builds on the pseudo-observation idea but significantly reduces overhead by using single-step diffusion models. This makes it $>10\times$ faster than per-step querying methods.
  • Progressive 3D Update: Unlike Deceptive-NeRF or 3DGS-Enhancer, $Difix3D+$ introduces a progressive 3D update pipeline. This iteratively refines the 3D representation, which is crucial for correcting artifacts even in extreme novel views and preserving long-range consistency.
  • Dual-Role Application: $Difix3D+$ applies its DiFIx model both during the 3D representation optimization (to distill improvements) and at render-time as a real-time neural enhancer, leading to superior visual quality and addressing residual artifacts.

3.3. Technological Evolution

The evolution of 3D reconstruction and novel-view synthesis can be traced through several stages:

1. Traditional Computer Graphics: Early methods relied on explicit geometric models (e.g., meshes, point clouds) often reconstructed from multi-view stereo (MVS) or Structure-from-Motion (SfM). These methods could generate novel views but often lacked photorealism and struggled with complex materials or translucent objects.

2. Implicit Neural Representations: The advent of NeRF [37] marked a paradigm shift. By representing scenes as implicit neural functions, NeRF achieved unprecedented photorealism and detail, especially for view synthesis. However, NeRF models are slow to train, slow to render, and prone to artifacts in underconstrained or extreme views.

3. Explicit Neural Representations & Efficiency: 3D Gaussian Splatting [20] emerged as a highly efficient explicit representation, offering real-time rendering while maintaining NeRF-level quality. Both NeRF and 3DGS still face the artifact problem in challenging viewing conditions.

4. Integration of Generative Priors: The success of 2D generative models, particularly diffusion models, has led to efforts to integrate their powerful semantic priors into 3D tasks. Initial attempts involved per-step guidance, which was computationally intensive. More recent trends, including this paper, focus on distilling these priors efficiently (e.g., via pseudo-observations or single-step models) to enhance 3D representations.

   $Difix3D+$ fits within this timeline by addressing the remaining artifact problem in NeRF/3DGS and improving the efficiency of integrating generative priors. It pushes the boundaries of photorealistic novel-view synthesis by making diffusion model enhancements practical for large-scale 3D reconstruction.

3.4. Differentiation Analysis

Compared to the main methods in related work, $Difix3D+$ offers several core differences and innovations:

• Efficient Diffusion Prior Integration: Unlike per-step querying methods (e.g., Nerfbusters [70], Zero-1-to-3 [25], Reconfusion [72]), $Difix3D+$ uses a single-step diffusion model (DiFIx) and integrates its priors by refining pseudo-training views that are then distilled back into the 3D representation. This makes the optimization phase significantly faster ($>10 \times$ faster than per-step querying methods).

• Progressive 3D Update Pipeline: A key innovation is the iterative, progressive refinement of the 3D representation. This strategy gradually expands the 3D cues that can be consistently rendered to novel views, enhancing the conditioning signal for the diffusion model and ensuring multi-view consistency even for extreme novel viewpoints. This directly addresses the inconsistency issues that arise when generative models hallucinate details.

• Dual-Role Application of DiFIx: DiFIx is unique in its dual application:

  1. During Optimization: It actively improves the underlying 3D representation by providing clean pseudo-views.
  2. During Inference: It acts as a real-time post-processing neural enhancer, effectively removing residual artifacts that even an improved 3D representation might still produce due to limited capacity or imperfect supervision. The single-step nature of DiFIx makes this post-processing feasible in near real-time (e.g., 76ms on an NVIDIA A100 GPU).

• Generalizability and Compatibility: The paper emphasizes that $DiFIx3D+$ is a general solution, compatible with both implicit (NeRF) and explicit (3DGS) 3D representations, showcasing its broad applicability.

• Data Curation Strategy: The comprehensive data curation strategies (e.g., Cycle Reconstruction, Model Underfitting, Cross Reference) for training DiFIx specifically on artifacts relevant to 3D novel view synthesis is a distinct methodological contribution.

  In essence, $Difix3D+$ provides a practical and highly efficient framework for injecting powerful 2D generative priors into 3D reconstruction, specifically targeting persistent artifact issues in novel-view synthesis while preserving 3D consistency and real-time inference capabilities.

4. Methodology

4.1. Principles

The core idea behind $Difix3D+$ is to leverage the powerful generative priors of a pretrained diffusion model to address artifacts in 3D reconstructions, especially in underconstrained regions or extreme novel views. The method operates on two main principles:

1. Artifact Removal through Single-Step Diffusion: A specialized single-step image diffusion model, named DiFIx, is fine-tuned to act as an image-to-image translator. It takes a rendered view with potential artifacts and a reference view as input, and outputs a refined, artifact-free novel view. The key intuition is that NeRF/3DGS artifacts resemble noisy images at a specific noise level, allowing a denoising diffusion model to effectively "fix" them.
2. Progressive 3D Consistency and Enhancement: The improvements from DiFIx are not merely applied as a post-processing step initially. Instead, they are distilled back into the underlying 3D representation through a progressive 3D update scheme. This iterative process ensures that the enhancements are multi-view consistent and gradually improve the 3D model itself. Additionally, DiFIx can be used as a final real-time post-processing step during inference to further sharpen details and remove residual artifacts.

4.2. Core Methodology In-depth (Layer by Layer)

The overall $Difix3D+$ pipeline (Figure 2) consists of two main stages: training DiFIx and then using it within a progressive 3D update scheme during 3D reconstruction, with an optional real-time post-processing step at inference.

The following figure (Figure 2 from the original paper) illustrates the overall pipeline of the $DIFIX3D+$ model, involving different stages for 3D optimization and real-time post-rendering enhancement.

该图像是来自 Difix3D+ 论文的示意图，展示了 Difix3D 用于 3D 优化和 Difix3D+ 用于实时后期渲染的流程。图中对比了参考视图、新颖视角与经过 Difix 模型处理后的效果，突出单步扩散模型在提升 3D 重建细节及去除伪影中的作用。

Blue Cameras: Training Views; Red Cameras: Target Views Orange Cameras: Intermediate Novel views along the progressive 3D updating trajectory (Sec. 4.2). Figure 2. DIFIx3D $^ +$ pipeline. The overall pipeline of the DIFIX $^ { 3 \mathrm { D + } }$ model involves the following stages: Step 1: Given a pretrained 3D representation, all-u n ri al ndov.

The pipeline initiates with a pre-trained 3D representation and then progresses through several steps to refine and enhance novel views.

4.2.1. DiFIX: From a pretrained diffusion model to a 3D Artifact Fixer

The first step is to adapt a pretrained diffusion model into an image-to-image translation model specifically designed to remove artifacts from neural renderings.

4.2.1.1. Model Architecture and Reference View Conditioning

The DiFIx model is built upon SD-Turbo [49], a single-step diffusion model known for its efficiency. The authors modify SD-Turbo to incorporate reference view conditioning.

The following figure (Figure 3 from the original paper) shows the architecture of the DiFIx model, which is fine-tuned from SD-Turbo and uses a frozen VAE encoder and a LoRA fine-tuned decoder.

该图像是一个示意图，展示了Difix3D+中利用单步扩散模型优化3D重建和新视角合成的整体流程。图中包括输入视角、参考视角及通过VAE编码器、U-Net、参考混合层和VAE解码器模块处理后生成的输出视角，体现了模型的特征转换和增强机制。

fine-tuned from SD-Turbo, using a frozen VAE encoder and a LoRA fine-tuned decoder.

As seen in the figure, the architecture involves a VAE encoder to transform input images into a latent space, a U-Net (the core diffusion model) for processing, and a LoRA fine-tuned decoder to reconstruct the image from the latent space.

To condition the model on reference views $I _ { \mathrm { r e f } }$, which are typically the closest training views to the target novel view $\tilde { I }$, the authors adapt the self-attention layers within the U-Net into a reference mixing layer. This is inspired by techniques used in video and multi-view diffusion models.

The mechanism for this reference mixing layer is as follows:

1. The novel view $\tilde { I }$ and the reference views $I _ { \mathrm { r e f } }$ are first concatenated along an additional view dimension.
2. This concatenated input is then encoded into a latent space using a VAE encoder: $\mathcal { E } ( ( \tilde { I } , I _ { \mathrm { r e f } } ) ) = \mathbf { z } \in \mathbb { R } ^ { V \times C \times H \times W }$.
   • $V$ is the total number of input views (including the novel view and all reference views).
   • $C$ is the number of latent channels.
   • $H$ and $W$ are the spatial dimensions in the latent space.
3. The reference mixing layer operates on this latent representation $\mathbf { z }$ by manipulating its dimensions before and after self-attention. Using einops [47] notation: $ \begin{array} { r l } & { \mathbf { z } ^ { \prime } \mathrm { r e a r r a n g e } ( \mathbf { z } , \mathrm { b ~ \subset ~ \mathbb { V } ~ \mathrm { (h w) } ~ } \mathrm { b ~ \subset ~ \mathbb { C } ~ \mathrm { (v h w) } ~ } ) } \ & { \mathbf { z } ^ { \prime } l _ { \phi } ^ { i } ( \mathbf { z } ^ { \prime } , \mathbf { z } ^ { \prime } ) } \ & { \mathbf { z } ^ { \prime } \mathrm { r e a r r a n g e } ( \mathbf { z } ^ { \prime } , \mathrm { b ~ \subset ~ \mathbb { C } ~ \mathrm { (v h w) } ~ } \mathrm { b ~ \subset ~ \mathbb { C } ~ \mathrm { v } ~ \mathrm { (h w) } ~ } ) , } \end{array} $ Here:
   • The first rearrange operation ($b c (v h w) -> b v (c h w)$) reshapes the tensor $\mathbf { z }$ to effectively treat the view dimension $V$ as part of the batch or spatial dimension. This means that features from different views are flattened and processed together as if they were spatial locations within a single, larger image. The (v h w) part indicates that the view, height, and width dimensions are combined. $b$ is the batch dimension, $c$ is the channel dimension.
   • $l _ { \phi } ^ { i } ( \mathbf { z } ^ { \prime } , \mathbf { z } ^ { \prime } )$ is a self-attention layer applied over this new vhw dimension. This allows the model to capture dependencies across views (and spatial locations within views) simultaneously, effectively mixing information from the novel view and reference views.
   • The second rearrange operation reverses the reshaping, returning the tensor to its original b c v h w format. This design allows DiFIx to inherit existing weights from the original 2D self-attention layers of SD-Turbo, making the adaptation efficient. This cross-view dependency capture is critical for handling degraded novel views and preserving visual consistency.

4.2.1.2. Fine-tuning Strategy

DiFIx is fine-tuned from SD-Turbo [49] in a manner similar to Pix2pix-Turbo [40].

• VAE Encoder and LoRA Decoder: A frozen VAE encoder is used to convert images to the latent space, and a LoRA (Low-Rank Adaptation) [77] fine-tuned decoder is used to convert latents back to images. LoRA is an efficient fine-tuning technique that adds small, low-rank matrices to the weights of a pretrained model, allowing for adaptation with minimal additional parameters and computational cost.

• Input and Noise Level: Instead of generating images from random Gaussian noise (as standard DMs do), DiFIx is trained to take a degraded rendered image $\tilde { I }$ directly as input. Crucially, a lower noise level of $\tau = 200$ is applied, rather than the maximum $\tau = 1000$ typically used for full noise.

• Intuition for Noise Level: The core insight is that artifacts from neural rendering (NeRF/3DGS) exhibit statistical properties similar to images corrupted with a specific level of Gaussian noise. By finding the optimal $\tau$, the model can effectively "denoise" these artifacts without altering the original image context too much or being overly conservative.

  The following figure (Figure 4 from the original paper) demonstrates the effect of varying noise levels on the denoising performance.

  该图像是论文中的实验结果对比图，展示了不同3D重建方法在多个场景的渲染效果。各列分别为GT、Nerfbusters、GANeRF、NeRFLiX、Nerfacto和本文方法（Ours），显示本文方法在去除伪影和细节还原方面表现更优。

Input τ = 600 τ= 400 τ = 200 τ = 10 τ 1000 800 600 400 200 10 PSNR 12.18 13.63 15.64 17.05 17.73 17.72 SSIM 0.4521 0.5263 0.6129 0.6618 0.6814 0.6752 Figure 4. Noise level. To validate our hypothesis that the distribution of images with NeRF/3DGS artifacts is similar to the distribution of noisy images used to train SD-Turbo [49], we perform single-step "denoising" at varying noise levels. At higher noise levels (e.g., $\tau = 600$), the model effectively removes artifacts but also alters the image context. At lower noise levels (e.g., $\tau = 10$), the model makes only minor adjustments, leaving most artifacts intact. $\tau = 200$ strikes a good balance, removing artifacts while preserving context, and achieves the highest metrics.

As shown in Figure 4, $\tau = 200$ yields the best visual results and highest PSNR/SSIM metrics, confirming this intuition. Higher noise levels (e.g., $\tau = 600$) can remove artifacts but also alter the image context (i.e., make unfaithful changes), while lower noise levels (e.g., $\tau = 10$) leave most artifacts intact.

4.2.1.3. Losses

The DiFIx model is supervised using a combination of 2D supervision losses applied to the model output $\hat { I }$ and the ground-truth image $I$. These losses are applied in the RGB image space.

1. Reconstruction Loss ($\mathcal { L } _ { \mathrm { R e c o n } }$): This is a simple L2 difference (Mean Squared Error) between the model's output and the ground truth. $ \mathcal { L } _ { \mathrm { { R e c o n } } } = | \hat { I } - I | _ { 2 } . $ Here:

   • $\hat { I }$ is the image output by the DiFIx model.
   • $I$ is the ground-truth clean image.
   • $\| \cdot \| _ { 2 }$ denotes the L2 norm, which calculates the Euclidean distance between the pixel values of the two images. It encourages pixel-wise accuracy.

2. Perceptual Loss ($\mathcal { L } _ { \mathrm { L P I P S } }$): This loss aims to capture perceptual similarity, which is often more aligned with human perception than simple pixel-wise differences. It uses features extracted from a pretrained VGG-16 network [52]. $ \mathcal { L } _ { \mathrm { L P I P S } } = \frac { 1 } { L } \sum _ { l = 1 } ^ { L } \alpha _ { l } \left. \phi _ { l } ( \hat { I } ) - \phi _ { l } ( I ) \right. _ { 1 } , $ Here:

   • $\phi _ { l } ( \cdot )$ represents the feature map extracted from the $l$-th layer of a pretrained VGG-16 network.
   • $L$ is the total number of layers used for feature extraction.
   • $\alpha _ { l }$ are weighting coefficients for each layer's contribution.
   • $\| \cdot \| _ { 1 }$ denotes the L1 norm of the difference between feature maps. This loss encourages the generated image to have similar high-level features as the ground truth, thus enhancing image details and overall perceptual quality.

3. Style Loss ($\mathcal { L } _ { \mathrm { G r a m } }$): This loss, based on Gram matrices [43] from VGG-16 features, encourages sharper details and a consistent visual style. $ \mathcal { L } _ { \mathrm { G r a m } } = \frac { 1 } { L } \sum _ { l = 1 } ^ { L } \beta _ { l } \left| \boldsymbol { G } _ { l } ( \hat { I } ) - \boldsymbol { G } _ { l } ( I ) \right| _ { 2 } , $ with the Gram matrix at layer $l$ defined as: $ G _ { l } ( I ) = \phi _ { l } ( I ) ^ { \top } \phi _ { l } ( I ) . $ Here:

   • $\boldsymbol { G } _ { l } ( I )$ is the Gram matrix computed from the feature map $\phi _ { l } ( I )$. The Gram matrix captures the correlations between different feature channels, effectively representing the "style" of an image.

   • $\beta _ { l }$ are weighting coefficients for each layer.

   • $\| \cdot \| _ { 2 }$ denotes the L2 norm of the difference between Gram matrices. This loss helps in matching texture, color, and other stylistic elements, encouraging sharper and more consistent details.

     The final loss used to train DiFIx is a weighted sum of these terms: $ \mathcal { L } = \mathcal { L } _ { \mathrm { R e c o n } } + \mathcal { L } _ { \mathrm { L P I P S } } + 0 . 5 \mathcal { L } _ { \mathrm { G r a m } } $ The coefficient 0.5 for $\mathcal { L } _ { \mathrm { G r a m } }$ indicates its relative importance compared to the other two losses.

4.2.2. Data Curation

To effectively train DiFIx, a large dataset of paired images (containing typical novel-view synthesis artifacts and their corresponding clean ground truth) is required. Since such a dataset is not readily available for extreme novel views, the authors devise several strategies to curate this data:

The following are the results from Table 1 of the original paper:

Table 1. Data curation. We curate a paired dataset featuring common artifacts in novel-view synthesis. For DL3DV scenes [23], we employ sparse reconstruction and model underfitting, while for internal real driving scene (RDS) data, we utilize cycle reconstruction, cross reference, and model underfitting techniques.

1. Sparse Reconstruction:

   • Method: Train a 3D representation (e.g., NeRF) using only a sparse subset (e.g., every $n$-th frame) of the available training images. The remaining (held-out) ground truth images are then paired with the rendered novel views from this sparsely trained model. These rendered views will likely contain artifacts due to insufficient training data.
   • Applicability: Effective for datasets like DL3DV [23] that have diverse camera trajectories, allowing for significant deviation between reference and target views.

2. Cycle Reconstruction:

   • Method: Primarily for nearly linear trajectories (e.g., autonomous driving datasets).
     • First, a 3D model (e.g., NeRF) is trained on the original camera trajectory.
     • Then, pseudo-views are rendered from this model along a shifted trajectory (e.g., 1-6 meters horizontally from the original path).
     • A second 3D model is trained against these rendered pseudo-views.
     • Finally, this second 3D model is used to render degraded views for the original camera trajectory, for which ground truth is available.
   • Applicability: Used for internal Real Driving Scene (RDS) datasets. This creates a synthetic degradation process where the original ground truth can be compared against a view synthesized by a model that "learned" from slightly distorted data.

3. Model Underfitting:

   • Method: To intentionally generate salient artifacts, the 3D reconstruction model is underfit by training it for a reduced number of epochs (e.g., 25%-75% of the full training schedule).
   • Applicability: Views rendered from this underfit reconstruction are then paired with their corresponding ground-truth images. This directly simulates the artifacts that arise from insufficient optimization.

4. Cross Reference:

   • Method: For multi-camera datasets, a 3D model is trained using data from only one camera. Images from the remaining held-out cameras (which have not been used for training) are then rendered as novel views.

   • Applicability: Used for internal RDS datasets. This strategy is effective when cameras have similar ISP (Image Signal Processor) settings to ensure visual consistency despite being from different camera feeds.

     These diverse strategies ensure that the curated dataset contains a wide range of common artifacts observed in novel-view synthesis (blurred details, missing regions, ghosting, spurious geometry), providing a robust learning signal for DiFIx.

4.2.3. DifIx3D+: NVS with Diffusion Priors

Once DiFIx is trained, it is integrated into the 3D reconstruction pipeline (Difix3D) and later used for real-time post-processing ($Difix3D+$).

4.2.3.1. DIFIx3D: Progressive 3D updates

Directly applying DiFIx to enhance rendered novel views during inference (without modifying the underlying 3D representation) can lead to inconsistencies across different poses/frames, especially in under-observed regions where DiFIx might hallucinate details. To address this, the outputs of DiFIx are distilled back into the 3D representation during training, improving multi-view consistency and perceptual quality.

The paper adopts an iterative training scheme, similar to Instruct-NeRF2NeRF [14], that progressively grows the set of 3D cues that can be rendered multi-view consistently to novel views. This progressively increases the conditioning available to the diffusion model.

The following are the details of the Progressive 3D Updates algorithm (Algorithm 1 from the supplementary material):

Algorithm 1: Progressive 3D Updates for Novel View Rendering Input: Reference views Vref, Target views Vtarget, 3D representation R (e.g., NeRF, 3DGS), Diffusion model D (DIFIX), Number of iterations per refinement Niter, Perturbation step size ∆pose Output: High-quality, artifact-free renderings at Vtarget 1 Initialize: Optimize 3D representation R using Vref. 2 while not converged do /* Optimize the 3D representation / 3 for i = 1 to Niter do 4 Optimize R using the current training set. / Generate novel views by perturbing camera poses */ 5 for each v Vtarget do 6 Find the nearest camera pose of v in the training set. 7 Perturb the nearest camera pose by ∆pose. 8 Render novel view ù using R. 9 Refine ù using diffusion model D. 10 Add refined view ò to the training set. 11 return Refined renderings at $V _ { \mathrm { t a r g e t } }$

Here's a detailed breakdown of Algorithm 1:

1. Initialization: The 3D representation $R$ (e.g., NeRF, 3DGS) is initially optimized using the provided reference views $V_{\mathrm{ref}}$. This establishes a base 3D model.

2. Iterative Optimization Loop (while not converged do): The core process runs iteratively until a convergence criterion is met. This criterion is not explicitly defined in the pseudocode but implies reaching a desired quality or a maximum number of steps.

   a. 3D Representation Optimization ($for i = 1 to Niter do$): * For Niter iterations, the 3D representation $R$ is optimized using the current training set. This training set initially contains $V_{\mathrm{ref}}$ but will be augmented in subsequent steps.

       b.  **Generate and Refine Novel Views ($for each v \in Vtarget do$):**
   *   For each `target view` $v$ in the set $V_{\mathrm{target}}$ (the desired `novel viewpoints`):
       *   **Find Nearest Camera Pose:** The camera pose closest to $v$ in the *current training set* (which includes original `reference views` and previously refined `pseudo-views`) is identified.
       *   **Perturb Camera Pose:** This nearest camera pose is `perturbed` by a small `step size` $\Delta\mathrm{pose}$ *towards* the `target view` $v$. This is a crucial step in the "progressive" nature of the updates, gradually moving the model's focus closer to the challenging target views.
       *   **Render Novel View:** A `novel view` $\tilde{I}$ is rendered from the current 3D representation $R$ using this `perturbed camera pose`. This $\tilde{I}$ will likely contain artifacts.
       *   **Refine with `DiFIx`:** The `rendered novel view` $\tilde{I}$ is passed through the `DiFIx diffusion model D` for refinement. `DiFIx` takes $\tilde{I}$ and a `reference view` (e.g., the original nearest training view) and outputs a `cleaned, enhanced view` $\hat{I}$.
       *   **Augment Training Set:** This `refined view` $\hat{I}$ (along with its `perturbed camera pose`) is added to the `training set` for the 3D representation $R$.
   

3. Return: Once the iterative process converges, the pipeline returns high-quality, artifact-free renderings at the target views $V_{\mathrm{target}}$.

   This progressive approach ensures that the 3D model learns to generalize effectively from progressively more challenging viewpoints, maintaining 3D consistency by distilling the generative priors from DiFIx into the underlying scene representation.

4.2.3.2. Difix3D+: With Real-time Post Render Processing

Even with the progressive 3D updates, some regions might still appear blurry or contain residual artifacts due to slight multi-view inconsistencies or the limited capacity of reconstruction methods to represent sharp details. To address this, $Difix3D+$ incorporates an additional, final step: using DiFIx as a post-processing step during render inference.

• Mechanism: When a novel view is rendered from the refined 3D representation, the resulting image is passed through the DiFIx model one last time.

• Efficiency: Since DiFIx is a single-step model (trained from SD-Turbo), this post-processing adds minimal overhead. The paper states it adds only 76ms on an NVIDIA A100 GPU, making it near real-time and over 10 times faster than using standard multi-step diffusion models.

• Benefits: This final step significantly enhances image sharpness and removes remaining residual artifacts, contributing to further improvements in perceptual metrics without compromising 3D coherence.

  The final output is a high-quality, photorealistic novel view that benefits from generative priors both during 3D optimization and real-time inference.

5. Experimental Setup

5.1. Datasets

The experiments are conducted on two main types of datasets: in-the-wild scenes and automotive scenes.

1. DL3DV Dataset [23]:

   • Source/Characteristics: A large-scale scene dataset designed for deep learning-based 3D vision, containing diverse in-the-wild scenes. It includes camera trajectories that allow for sampling novel views with significant deviation from training views.
   • Usage: Used for training the general DiFIx model (80% of scenes, 112 out of 140) and evaluating in-the-wild artifact removal. 80,000 noisy-clean image pairs were generated using Sparse Reconstruction and Model Underfitting data curation strategies. Evaluation was performed on the remaining 28 held-out scenes.
   • Data Example: (While not explicitly shown in the paper, typical DL3DV scenes might include indoor environments, outdoor landscapes, and object-centric captures with varying camera movements, allowing for complex novel view synthesis.)

2. Nerfbusters Dataset [70]:

   • Source/Characteristics: A dataset specifically curated to highlight artifacts in NeRF models. It contains challenging casual captures that often lead to ghostly artifacts in NeRF renderings.
   • Usage: Used for evaluation of in-the-wild artifact removal (12 captures) and for ablation studies. Reference and target views were selected following its recommended protocol, often involving significant deviations.
   • Data Example: (Again, no explicit example, but images would likely contain visual clutter, inconsistent lighting, or moving objects that challenge NeRF's static scene assumption, leading to artifacts.)

3. Internal Real Driving Scene (RDS) Dataset:

   • Source/Characteristics: An in-house dataset created by the authors, featuring automotive scenes. It consists of multi-camera captures (e.g., three cameras with 40-degree overlaps).

   • Usage: Used for training a specialized DiFIx model for automotive scene enhancement (40 scenes). 100,000 image pairs were generated using Cycle Reconstruction, Cross Reference, and Model Underfitting strategies. Evaluation was performed on 20 held-out scenes, with NeRF trained on the center camera and evaluation on the other two cameras as novel views.

   • Data Example: (Images would be typical street scenes captured from a moving vehicle, likely showing cars, roads, buildings, and pedestrians from multiple perspectives.)

     The choice of DL3DV and Nerfbusters ensures evaluation on diverse in-the-wild scenarios, specifically targeting artifact-prone cases. The RDS dataset demonstrates the method's applicability and robustness in a crucial real-world domain like autonomous driving, where novel view synthesis from various camera angles is critical. The data curation strategies are designed to specifically generate noisy-clean pairs that simulate typical novel-view synthesis artifacts, making them highly effective for training DiFIx.

5.2. Evaluation Metrics

For every evaluation metric mentioned in the paper, here is a complete explanation:

1. Peak Signal-to-Noise Ratio (PSNR)

   • Conceptual Definition: PSNR quantifies the quality of a reconstructed image by comparing it to a ground-truth image. It measures the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Higher PSNR values indicate a better reconstruction quality, meaning the generated image is closer to the original ground truth at a pixel level. It is highly sensitive to small pixel differences.
   • Mathematical Formula: $ \mathrm { P S N R } = 1 0 \cdot \log _ { 1 0 } \left( \frac { \mathrm { MAX } ^ { 2 } } { \mathrm { MSE } } \right) , $
   • Symbol Explanation:
     • MAX: The maximum possible pixel value of the image. For an 8-bit image, this is 255.
     • MSE: The Mean Squared Error between the predicted image $I _ { \mathrm { p r e d } }$ and the ground truth image $I _ { \mathrm { { g t } } }$. It is calculated as: $ \mathrm { MSE } = \frac { 1 } { H \times W \times C } \sum _ { i = 1 } ^ { H } \sum _ { j = 1 } ^ { W } \sum _ { k = 1 } ^ { C } ( I _ { \mathrm { p r e d } } ( i , j , k ) - I _ { \mathrm { g t } } ( i , j , k ) ) ^ { 2 } $ Where $H$, $W$, and $C$ are the height, width, and number of channels (e.g., 3 for RGB) of the images, and I (i, j, k) is the pixel value at location (i, j) in channel $k$.

2. Structural Similarity Index (SSIM) [67]

   • Conceptual Definition: SSIM evaluates the perceived quality of an image by assessing its structural similarity to a ground-truth image. Unlike PSNR which focuses on absolute errors, SSIM considers three key components of image perception: luminance (brightness), contrast, and structure. It is a more perceptually relevant metric than PSNR as it attempts to mimic the human visual system. Values range from -1 to 1, with 1 indicating perfect similarity.
   • Mathematical Formula: $ \mathrm { S S I M } ( I _ { \mathrm { p r e d } } , I _ { \mathrm { g t } } ) = \frac { ( 2 \mu _ { \mathrm { p r e d } } \mu _ { \mathrm { g t } } + C _ { 1 } ) ( 2 \sigma _ { \mathrm { p r e d , g t } } + C _ { 2 } ) } { ( \mu _ { \mathrm { p r e d } } ^ { 2 } + \mu _ { \mathrm { g t } } ^ { 2 } + C _ { 1 } ) ( \sigma _ { \mathrm { p r e d } } ^ { 2 } + \sigma _ { \mathrm { g t } } ^ { 2 } + C _ { 2 } ) } , $
   • Symbol Explanation:
     • $I _ { \mathrm { p r e d } }$: The predicted image.
     • $I _ { \mathrm { g t } }$: The ground-truth image.
     • $\mu _ { \mathrm { p r e d } }$: The mean of $I _ { \mathrm { p r e d } }$.
     • $\mu _ { \mathrm { g t } }$: The mean of $I _ { \mathrm { g t } }$.
     • $\sigma _ { \mathrm { p r e d } } ^ { 2 }$: The variance of $I _ { \mathrm { p r e d } }$.
     • $\sigma _ { \mathrm { g t } } ^ { 2 }$: The variance of $I _ { \mathrm { g t } }$.
     • $\sigma _ { \mathrm { p r e d , g t } }$: The covariance between $I _ { \mathrm { p r e d } }$ and $I _ { \mathrm { g t } }$.
     • $C _ { 1 } = ( K _ { 1 } L ) ^ { 2 }$ and $C _ { 2 } = ( K _ { 2 } L ) ^ { 2 }$: Small constants included to avoid division by zero or numerical instability. $L$ is the dynamic range of the pixel values (e.g., 255 for 8-bit images), and K _ { 1 }, K _ { 2 } are small constants (e.g., 0.01, 0.03).

3. Learned Perceptual Image Patch Similarity (LPIPS) [19]

   • Conceptual Definition: LPIPS measures the perceptual similarity between two images using feature embeddings extracted from pretrained neural networks (like VGG-16). It attempts to correlate better with human perceptual judgments of image quality than traditional metrics like PSNR or SSIM. Lower LPIPS values indicate greater perceptual similarity.
   • Mathematical Formula: $ \mathrm { L P I P S } ( I _ { \mathrm { p r e d } } , I _ { \mathrm { g t } } ) = \sum _ { l } | \phi _ { l } ( I _ { \mathrm { p r e d } } ) - \phi _ { l } ( I _ { \mathrm { g t } } ) | _ { 2 } ^ { 2 } , $
   • Symbol Explanation:
     • $I _ { \mathrm { p r e d } }$: The predicted image.
     • $I _ { \mathrm { g t } }$: The ground-truth image.
     • $\phi _ { l } ( \cdot )$: The feature map extracted from the $l$-th layer of a pretrained VGG-16 network [52] (or other deep network like AlexNet or SqueezeNet). These features capture high-level semantic information.
     • $\sum _ { l }$: Summation over different layers (or scales) of the feature extractor.
     • $\| \cdot \| _ { 2 } ^ { 2 }$: The squared L2 norm (Euclidean distance) of the difference between the feature maps.

4. Fréchet Inception Distance (FID) [15]

   • Conceptual Definition: FID is a metric used to assess the quality of images generated by generative models (like diffusion models). It measures the statistical similarity between the distribution of generated images and the distribution of real images. It does this by computing the Fréchet distance (also known as Wasserstein-2 distance) between two multivariate Gaussians fitted to feature representations of the real and generated images. These features are typically extracted from a pretrained Inception-v3 network. Lower FID values indicate that the generated image distribution is closer to the real image distribution, implying higher quality and realism.
   • Mathematical Formula: $ \mathrm { F I D } = \Vert \mu _ { \mathrm { g e n } } - \mu _ { \mathrm { r e a l } } \Vert _ { 2 } ^ { 2 } + \mathrm { T r } ( \Sigma _ { \mathrm { g e n } } + \Sigma _ { \mathrm { r e a l } } - 2 ( \Sigma _ { \mathrm { g e n } } \Sigma _ { \mathrm { r e a l } } ) ^ { \frac { 1 } { 2 } } ) , $
   • Symbol Explanation:
     • $\mu _ { \mathrm { g e n } }$: The mean of the feature vectors for the generated images (extracted from an Inception-v3 network).
     • $\mu _ { \mathrm { r e a l } }$: The mean of the feature vectors for the real (ground-truth) images.
     • $\Vert \cdot \Vert _ { 2 } ^ { 2 }$: The squared L2 norm of the difference between the mean vectors.
     • $\Sigma _ { \mathrm { g e n } }$: The covariance matrix of the feature vectors for the generated images.
     • $\Sigma _ { \mathrm { r e a l } }$: The covariance matrix of the feature vectors for the real images.
     • $\mathrm { T r } ( \cdot )$: The trace of a matrix (sum of its diagonal elements).
     • $(\Sigma _ { \mathrm { g e n } } \Sigma _ { \mathrm { r eal } }) ^ { \frac { 1 } { 2 } }$: The matrix square root of the product of the two covariance matrices.

5. Thresholded Symmetric Epipolar Distance (TSED) [80]

   • Conceptual Definition: TSED is a metric specifically designed to evaluate multi-view consistency in novel-view synthesis. It quantifies the number of consistent frame pairs in a sequence. A pair of frames is considered consistent if corresponding points between them satisfy the epipolar constraint within a given threshold. A higher TSED score indicates better multi-view consistency.
   • Mathematical Formula: The paper refers to [80] for its definition. The core idea of epipolar distance is that for any point in one image, its corresponding point in another image (of the same scene from a different viewpoint) must lie on a specific line, called the epipolar line. The symmetric epipolar distance measures the distance from a point in one image to the epipolar line defined by its corresponding point in the other image, and vice-versa. TSED then thresholds this distance to count consistent pairs. Let $x_1$ and $x_2$ be corresponding points in two images, and $F$ be the fundamental matrix relating the two views. The epipolar line for $x_1$ in the second image is $l_2 = F x_1$, and for $x_2$ in the first image is $l_1 = F^T x_2$. The symmetric epipolar distance is often defined as: $ d_{sym} = \frac{(x_2^T F x_1)^2}{(F x_1)_1^2 + (F x_1)_2^2} + \frac{(x_2^T F x_1)^2}{(F^T x_2)_1^2 + (F^T x_2)_2^2} $ TSED then involves: $ \mathrm { TSED } ( \mathrm { images } ) = \frac { 1 } { N _ { \mathrm { pairs } } } \sum _ { \mathrm { pairs } } \mathbb { I } ( d _ { \mathrm { sym } } < \text { Threshold } ) $
   • Symbol Explanation:
     • $x_1, x_2$: Homogeneous coordinates of corresponding points in two images.
     • $F$: The fundamental matrix between the two camera views.
     • $(F x_1)_1^2 + (F x_1)_2^2$: The sum of squares of the first two components of the vector $F x_1$, representing the square of the perpendicular distance from $x_2$ to the epipolar line $F x_1$.
     • $\mathbb { I } ( \cdot )$: The indicator function, which is 1 if the condition inside is true, and 0 otherwise.
     • Threshold: A predefined maximum allowable epipolar distance for points to be considered consistent (e.g., 2 or 4 pixels).
     • $N _ { \mathrm { pairs } }$: Total number of corresponding point pairs evaluated.
     • Higher TSED implies more consistent frame pairs. The paper specifically evaluates at $Threshold = 2$, $Threshold = 4$, and $Threshold = 8$.

Evaluation Protocol Notes: Following the Nerfbusters [70] evaluation procedure, a visibility map is calculated, and invisible regions are masked out when computing the metrics. This ensures that metrics are only calculated for visible parts of the scene, preventing inflated scores from regions that are inherently unobservable.

5.3. Baselines

The paper compares $Difix3D+$ against several representative baseline methods:

1. Nerfacto [58]: A highly optimized and modular framework for Neural Radiance Field development, often serving as a strong NeRF baseline. It represents the state-of-the-art in NeRF-based novel view synthesis without external priors. $Difix3D+$ uses Nerfacto as one of its backbones.

2. 3DGS [20]: 3D Gaussian Splatting is a recent explicit 3D representation method that provides real-time radiance field rendering with high quality. $Difix3D+$ uses 3DGS as its second backbone, demonstrating its generality across different 3D representation types.

3. Nerfbusters [70]: This method focuses on removing ghostly artifacts from casually captured NeRFs. It uses a 3D diffusion model to enhance NeRF outputs. It represents a baseline that also attempts artifact removal using diffusion priors but does so in a potentially more time-consuming (3D diffusion model) or less consistent manner than $Difix3D+$.

4. GANeRF [46]: This approach trains a per-scene generative adversarial network (GAN) to enhance the realism of the scene representation derived from NeRF. It represents a GAN-based approach to improving NeRF quality.

5. NeRFLiX [88]: This method improves novel view synthesis quality by aggregating information from nearby reference views at inference time. It is a deterministic method that enhances rendered views using multi-view information, but without generative priors for hallucination.

Implementation Details for Baselines: The authors state they use the gsplat library for 3DGS-based experiments and the official implementations for all other methods and baselines, ensuring fair comparisons.

These baselines are representative because they cover different strategies for novel view synthesis and artifact reduction: Nerfacto and 3DGS are core reconstruction methods; Nerfbusters and GANeRF use generative priors for enhancement (diffusion/GAN); and NeRFLiX uses multi-view aggregation. This allows for a comprehensive evaluation of $Difix3D+$'s performance and unique contributions.

6. Results & Analysis

6.1. Core Results Analysis

The experimental results demonstrate that $Difix3D+$ significantly outperforms existing baselines in enhancing 3D reconstruction and novel-view synthesis, particularly in perceptual quality and visual fidelity, while maintaining 3D consistency.

The following are the results from Table 2 of the original paper:

Table 2 Analysis (Nerfbusters and DL3DV Datasets):

• Significant FID Improvement: $Difix3D+$ (both Nerfacto and 3DGS variants) shows the most dramatic improvement in FID score. For example, on Nerfbusters, Difix3D+ (Nerfacto) achieves 49.44 compared to Nerfacto's 134.65 and Nerfbusters's 116.83. This represents an almost 3x reduction in FID relative to the base Nerfacto and indicates a significantly higher perceptual realism and closer distribution to ground-truth images. Similar trends are observed on the DL3DV dataset.

• LPIPS Reduction: $Difix3D+$ also achieves the lowest LPIPS scores across the board (e.g., 0.2789 for Nerfacto and 0.2637 for 3DGS on Nerfbusters). A lower LPIPS indicates better perceptual similarity to the ground truth, suggesting that $Difix3D+$'s output is more aligned with human visual perception.

• PSNR and SSIM Gains: While FID and LPIPS measure perceptual quality, PSNR and SSIM assess pixel-wise fidelity and structural similarity. $Difix3D+$ still shows consistent improvements in PSNR (e.g., about +1dB over Nerfacto baseline) and SSIM, demonstrating that it not only generates perceptually realistic images but also maintains high fidelity to the original scene content, avoiding excessive hallucination that deviates from the true geometry.

• Generality Across Backbones: The performance gains are observed for both Nerfacto (an implicit NeRF representation) and 3DGS (an explicit Gaussian representation) backbones, confirming $Difix3D+$'s claim of being a general solution. The 3DGS variant generally achieves slightly better results, especially in SSIM.

• Advantage of $Difix3D+$ over Difix3D: Comparing Difix3D (which only uses progressive 3D updates) with $Difix3D+$ (which adds real-time post-rendering processing), $Difix3D+$ consistently shows further improvements in all metrics, especially LPIPS and FID, validating the effectiveness of the neural enhancer step during inference.

  The following figure (Figure 5 from the original paper) provides qualitative comparisons of different methods.

  该图像是论文中的实验结果对比图，展示了不同3D重建方法在多个场景的渲染效果。各列分别为GT、Nerfbusters、GANeRF、NeRFLiX、Nerfacto和本文方法（Ours），显示本文方法在去除伪影和细节还原方面表现更优。

Figure 5. Qualitative results on the Nerfbusters [70] dataset (top) and DL3DV dataset (bottom). DIFIX3D $^ +$ corrects significantly more artifacts that other methods.

Qualitative Results (Figure 5): Figure 5 visually supports the quantitative findings, showing that $Difix3D+$ significantly corrects more artifacts than other methods (e.g., Nerfbusters, GANeRF, NeRFLiX, Nerfacto). $Difix3D+$ renderings appear sharper, more coherent, and free from common NeRF/3DGS artifacts like blurriness, ghosting, or spurious geometry, particularly in underconstrained regions.

The following are the results from Table 3 of the original paper:

Table 3 Analysis (Automotive Scene Enhancement on RDS Dataset):

• Strong Performance in Specialized Domain: Similar to in-the-wild scenes, $Difix3D+$ with a Nerfacto backbone demonstrates superior performance on the RDS dataset. It achieves the highest PSNR (21.75), SSIM (0.5829), and the lowest LPIPS (0.4016) and FID (73.08).

• Comparison to NeRFLiX: While Nerfacto + NeRFLiX improves PSNR and SSIM over plain Nerfacto, its FID score (116.28) actually increases, indicating that its deterministic enhancements might not fully capture the realism of the ground-truth distribution. In contrast, $Difix3D+$ significantly reduces FID, highlighting its strength in generating perceptually realistic images.

• Impact of Difix3D vs $Difix3D+$: Again, the addition of real-time post-rendering ($Difix3D+$) provides further gains over just progressive 3D updates (Difix3D), reinforcing the value of the final enhancement step.

  The following figure (Figure 6 from the original paper) shows qualitative results on the RDS dataset.

  该图像是论文中对比示意图，展示了NeRFacto与Difix3D+在RDS数据集上的渲染效果。图中Difix3D+显著提升了视角变化下的照片真实感和细节清晰度。

Figure 6. Qualitative results on the RDS dataset. DIFIX for RDS was trained on 40 scenes and 100,000 paired data samples.

Qualitative Results (Figure 6): Figure 6 visually confirms the effectiveness of $Difix3D+$ in automotive scenes. The rendered views exhibit greater photorealism and detail clarity compared to Nerfacto, especially in complex regions like vehicle surfaces and environmental elements, and critically, maintains consistency across different views in a driving scenario.

Overall, the results strongly validate $Difix3D+$'s effectiveness in enhancing 3D reconstructions. Its core advantage lies in its ability to leverage powerful 2D generative priors efficiently (via single-step diffusion) and consistently (via progressive 3D updates and reference conditioning), leading to superior perceptual quality and fidelity across diverse scenes and representation types.

6.2. Data Presentation (Tables)

The following are the results from Table 2 of the original paper:

The following are the results from Table 3 of the original paper:

6.3. Ablation Studies / Parameter Analysis

The authors conduct comprehensive ablation studies to validate the effectiveness of individual components of the $Difix3D+$ pipeline and the DiFIx model.

6.3.1. Ablation of Pipeline Components

The following are the results from Table 4 of the original paper:

Table 4 Analysis (Ablation Study of $Difix3D+$ on Nerfbusters): This table incrementally adds components to a Nerfacto baseline to show their individual contributions:

• Nerfacto Baseline: The starting point, exhibiting the highest LPIPS and FID.
• + (a) (DIFIX) (Direct Application): Simply applying the DiFIx model as a post-processor to Nerfacto's raw renderings. This yields a substantial drop in LPIPS (0.4021 to 0.2996) and FID (134.65 to 49.87).
  • Insight: This demonstrates the raw power of DiFIx in enhancing perceptual quality. However, the text mentions that this direct application can lead to inconsistencies and flickering across frames, especially in less observed regions, as DiFIx hallucinates without 3D consistency feedback.
• + (a) + (b) (DiFIX + single-step 3D update) (Non-Incremental Distillation): This step involves distilling DiFIx outputs back into the 3D model, but in a non-incremental fashion (pseudo-views are added all at once). While PSNR and SSIM improve, LPIPS and FID worsen compared to direct DiFIx application (0.2996 to 0.3424 for LPIPS, 49.87 to 75.94 for FID).
  • Insight: This highlights the crucial role of the incremental progressive 3D update strategy. Adding pseudo-views all at once, without carefully guiding the 3D model, can introduce new inconsistencies or overwhelm the 3D model, leading to degraded perceptual quality.
• $+ (a) + (b) + (c) (DIFIX3D)$ (Progressive 3D Updates): This is the full DIFIX3D pipeline, incorporating the incremental progressive 3D updates. It significantly improves LPIPS and FID compared to the non-incremental approach (0.3424 to 0.3277 for LPIPS, 75.94 to 63.77 for FID).
  • Insight: This confirms that the progressive 3D update scheme is essential for effectively distilling the generative priors while maintaining 3D consistency. It leads to a better underlying 3D representation.
• $+ (a) + (b) + (c) + (d) (D1FIX3D+)$ (Real-time Post-Rendering): This is the complete $Difix3D+$ pipeline, adding the final real-time post-processing step with DiFIx. It achieves the best results across all metrics (PSNR 18.32, SSIM 0.6623, LPIPS 0.2789, FID 49.44).

  • Insight: This demonstrates that even after robust 3D updates, a final neural enhancer pass with DiFIx can effectively remove residual artifacts and enhance sharpness without compromising 3D coherence, leading to the highest perceptual quality.

    The following figure (Figure 7 from the original paper) shows a qualitative ablation of real-time post-render processing.

    该图像是图7的定性消融实验插图，展示了实时后渲染处理对渲染结果的影响。通过Difix3D+的神经增强器步骤，有效去除残余伪影，提升了PSNR并降低了LPIPS，绿色和红色框中的图像为对应区域放大细节。

Figure 7. Qualitative ablation of real-time post-render processing: DifIx $^ { 3 \\mathrm { D + } }$ uses an additional neural enhancer step that effectively removes residual artifacts, resulting in higher PSNR and lower LPIPS scores. The images displayed in green or red boxes correspond to zoomed-in views of the bounding boxes drawn in the main images.

Qualitative Ablation (Figure 7): Figure 7 visually illustrates the benefit of the real-time post-render processing step ($+ (d)$). The $Difix3D+$ output is noticeably sharper and more detailed in the highlighted regions compared to Difix3D, confirming the metric improvements from Table 4.

The following figure (Figure 8 from the original paper) shows a qualitative ablation results of $DiFIx3D+$ comparing different pipeline stages.

该图像是论文中Figure 8的示意图，展示了DiFIx3D+方法的定性消融结果。图中比较了不同方法对椅子模型细节的恢复效果，突出DiFIx3D+在清除伪影和细节增强上的优势。

Figure 8. Qualitative ablation results of DiFIx3D $^ +$ : The columns, labeled by method name, correspond to the rows in Tab. 4.

Qualitative Ablation (Figure 8): Figure 8 provides a visual comparison corresponding to the rows in Table 4. It clearly shows how each component contributes: the initial NeRF baseline has significant artifacts. Direct DiFIx application (a) removes many artifacts but might introduce inconsistencies. DiFIX + single-step 3D update (a+b) shows some improvement but might still be noisy. DIFIX3D (a+b+c) provides a more coherent and artifact-free reconstruction. Finally, $DIFIX3D+$ (a+b+c+d) delivers the most photorealistic and artifact-free result, showcasing the combined power of the pipeline.

6.3.2. Ablation of DiFIx Components

The following are the results from Table 5 of the original paper:

Table 5 Analysis (Ablation Study of DiFIx Components on Nerfbusters): This table investigates the design choices within the DiFIx model itself:

• pix2pix-Turbo Baseline: This is SD-Turbo fine-tuned similar to Pix2pix-Turbo with a high noise level ($\tau = 1000$) and without Gram loss or reference view conditioning. It serves as the starting point for DiFIx's development.
• Difix ($\tau = 200$): Simply decreasing the noise level from 1000 to 200 (while keeping other factors out) results in a significant improvement in LPIPS (0.3810 to 0.3190) and FID (108.86 to 61.80).
  • Insight: This strongly validates the hypothesis from Figure 4 that NeRF/3DGS artifacts align with a specific, lower noise distribution, allowing the denoising model to operate more effectively without hallucinating wildly.
• Difix (with Gram): Adding the Gram loss to the DiFIx model (with $\tau = 200$) further reduces LPIPS (0.3190 to 0.3064) and FID (61.80 to 55.45).
  • Insight: This confirms the effectiveness of the Gram loss in encouraging sharper details and better capturing the style of the ground-truth images.
• Difix (with Gram and Ref): Incorporating reference view conditioning (using the reference mixing layer) along with Gram loss and $\tau = 200$ leads to the best DiFIx performance (LPIPS 0.2996, FID 47.87).

  • Insight: Reference view conditioning is crucial for providing DiFIx with additional context and geometric cues from clean views, allowing it to correct structural inaccuracies and color shifts more effectively.

    The following figure (Figure S1 from the supplementary material) provides visual comparisons for the DiFIx components.

    该图像是图表，展示了Difix模型不同组件对图像质量的影响。通过比较带参考图、缺少参考图、缺少Gram损失以及不同噪声水平au设置下的结果，说明降低噪声水平和添加Gram损失等改进均能提升图像清晰度与细节表现。

Figure S1. Visual comparison of DiFIx components. Reducing the noise level $\tau$ ((c) vs. (d)), incorporating Gram loss (b) vs. (c)), and conditioning on reference views ((a) vs. (b)) all improve our model.

Qualitative Ablation (Figure S1): Figure S1 visually supports the findings from Table 5.

• (c) vs. (d) (Noise Level): Shows that lowering $\tau$ (from pix2pix-Turbo's default 1000 in (c) to 200 in (d)) significantly improves artifact removal and visual quality, confirming the finding that NeRF/3DGS artifacts are best treated at an intermediate noise level.
• (b) vs. (c) (Gram Loss): Demonstrates how Gram loss (added in (c)) contributes to sharper details and a more refined texture compared to (b) without it.
• (a) vs. (b) (Reference Conditioning): Highlights that reference view conditioning (added in (b)) helps DiFIx to correct structural inconsistencies and better align colors/textures by leveraging clean reference information.

6.3.3. Multi-View Consistency Evaluation

The following are the results from Table S1 of the original paper:

Table S1 Analysis (Multi-view consistency on DL3DV): This table evaluates multi-view consistency using the Thresholded Symmetric Epipolar Distance (TSED) metric, where a higher score indicates better consistency.

• Difix3D and $Difix3D+$ Superiority: Difix3D consistently achieves higher TSED scores than Nerfacto and other baselines (e.g., 0.2601 vs 0.2492 for $TSED (Threshold = 2)$). $Difix3D+$ further improves these scores (e.g., 0.2654 for $TSED (Threshold = 2)$).
• Consistency Maintenance: This is a crucial finding, as generative models are often criticized for sacrificing consistency for realism. $Difix3D+$ demonstrates that its progressive 3D update scheme effectively distills generative priors while preserving, and even improving, multi-view consistency. The final post-processing step of $Difix3D+$ enhances sharpness without compromising this 3D coherence.
• Comparison to Other Baselines: NeRFLiX and GANeRF show comparable or slightly lower TSED scores than the Difix3D variants, highlighting that $Difix3D+$'s approach of distilling diffusion priors into the 3D representation is more effective at maintaining consistency than alternative enhancement methods.

7. Conclusion & Reflections

7.1. Conclusion Summary

$Difix3D+$ introduces a novel and highly effective pipeline for enhancing 3D reconstruction and novel-view synthesis. At its core is DiFIx, a single-step image diffusion model that functions as an artifact fixer. This model is adept at removing neural rendering artifacts from both NeRF and 3DGS representations. The pipeline leverages DiFIx in two critical ways:

1. Progressive 3D Updates: DiFIx is used during the reconstruction phase to clean pseudo-training views, which are then distilled back into the 3D representation through an iterative, progressive scheme. This significantly enhances underconstrained regions and improves the overall 3D representation quality while ensuring multi-view consistency.

2. Real-Time Post-Processing: During inference, DiFIx acts as a neural enhancer in a real-time post-processing step, effectively removing residual artifacts and boosting perceptual quality.

   The method's generality, efficiency (due to single-step diffusion), and ability to maintain 3D consistency are significant achievements. $Difix3D+$ demonstrates an average 2x improvement in FID score over baselines, alongside gains in PSNR, SSIM, and reductions in LPIPS, making it a state-of-the-art solution for photorealistic novel-view synthesis.

7.2. Limitations & Future Work

The authors acknowledge the following limitations and propose future research directions:

• Dependence on Initial 3D Reconstruction Quality: $Difix3D+$ is primarily a 3D enhancement model. Its performance is inherently limited by the quality of the initial 3D reconstruction. It currently struggles to enhance views where the 3D reconstruction has entirely failed (e.g., completely missing geometry or severely distorted regions).

  • Future Work: Addressing this limitation by integrating modern diffusion model priors (e.g., stronger generative capabilities for hallucinating plausible geometry where reconstruction is totally absent) is an exciting direction. This might involve guiding the initial 3D reconstruction more strongly with generative priors from the outset, rather than just using them for refinement.

• Single-Step Image Diffusion Model: To prioritize speed and enable near real-time post-rendering processing, DiFIx is derived from a single-step *image* diffusion model.

  • Future Work: Scaling DiFIx to a single-step *video* diffusion model is a promising avenue. This would enable enhanced long-context 3D consistency across entire sequences, potentially reducing any residual flickering or inconsistencies that might still occur between frames, even with the current progressive 3D updates.

7.3. Personal Insights & Critique

This paper presents a highly practical and effective approach to a critical problem in 3D reconstruction: the persistent presence of artifacts in novel views. The core strength lies in its intelligent integration of 2D generative priors from diffusion models into the 3D pipeline.

Key Strengths:

• Efficiency: The reliance on single-step diffusion for DiFIx is a major advantage. It allows for real-time post-processing and significantly speeds up the distillation process compared to multi-step diffusion or per-step querying methods. This makes $Difix3D+$ much more viable for real-world applications requiring speed.
• Multi-view Consistency: The progressive 3D update pipeline is a brilliant solution to the common challenge of generative models introducing inconsistencies. By distilling the enhanced views back into the 33D representation iteratively, the method ensures that improvements are geometrically consistent and not just 2D image-level hallucinations. The TSED metric results strongly support this claim.
• Generality: Its compatibility with both NeRF (implicit) and 3DGS (explicit) representations broadens its applicability and highlights the robustness of the DiFIx model itself.
• Data Curation: The detailed data curation strategies are crucial for training a specialized artifact fixer. This pragmatic approach to generating noisy-clean pairs is a valuable contribution to the methodology.

Potential Issues/Areas for Improvement:

• Dependence on Initial Reconstruction: While acknowledged as a limitation, the "garbage in, garbage out" principle still applies to some extent. If the initial 3D reconstruction is extremely poor (e.g., completely missing large regions), even DiFIx might struggle to hallucinate a plausible scene without significant prior knowledge beyond reference views. This suggests that for truly challenging captures, upstream reconstruction methods might also need improvement.
• Fine-tuning Cost for Specific Domains: While DiFIx is general, the RDS dataset required specific DiFIx training. This implies that for highly specialized domains, retraining DiFIx might be necessary, though the "few hours on a single consumer graphics card" claim suggests this is not a prohibitive cost.
• Subjectivity of Perceptual Metrics: While FID and LPIPS correlate well with human perception, they are still metrics. The qualitative results are compelling, but further user studies could solidify the perceptual quality claims.
• Definition of "Converged" in Algorithm 1: The pseudocode for Algorithm 1 uses while not converged do. The specific convergence criteria (e.g., change in loss, number of iterations, visual quality threshold) are not detailed in the main paper, which might impact reproducibility or tuning.

Transferability and Applications: The methods and conclusions of $Difix3D+$ are highly transferable.

• Any Neural Rendering Task: The DiFIx model could be adapted for artifact removal in other neural rendering contexts beyond NeRF/3DGS, such as neural avatars, dynamic scenes, or video generation from 3D.

• Image Enhancement: The DiFIx model itself, trained to fix common image degradations that mimic diffusion noise, could be used as a general image enhancement or restoration tool for tasks like denoising, deblurring, or inpainting, especially given its single-step efficiency.

• Creative Content Generation: The ability to hallucinate plausible details in underconstrained regions with 3D consistency opens doors for more robust creative content generation workflows, allowing artists to render high-quality assets from sparse data or extreme camera angles.

• Robotics/Autonomous Driving: The demonstrated success on the RDS dataset makes this approach particularly relevant for autonomous driving and robotics, where precise and artifact-free 3D environment reconstruction is crucial for perception and planning.

  Overall, $Difix3D+$ represents a significant step forward in making photorealistic 3D reconstruction more practical and robust by cleverly harnessing the power of efficient generative models.