DiffPBR: Point-Based Rendering via Spatial-Aware Residual Diffusion
Authors: Yiping Xie
Yuchi Huo
Yunlong Ran
Zijian Huang
Lincheng Li
Yingfeng Chen
Jiming Chen
Qi Ye
Venue: ICLR 2026 Poster (2026)
Keywords: Point-based graphics
Novel view synthesis
Neural rendering
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A novel method for point cloud rendering

Abstract: Neural radiance fields and 3D Gaussian splatting (3DGS) have significantly advanced 3D reconstruction and novel view synthesis (NVS). Yet, achieving high-fidelity and view-consistent renderings directly from point clouds---without costly per-scene optimization---remains a core challenge. In this work, we present DiffPBR, a diffusion-based framework that synthesizes coherent, photorealistic renderings from diverse point cloud inputs. We demonstrate that diffusion models, when guided by viewpoint-projected noise explicitly constrained by scene geometry and visibility, naturally enforce geometric consistency across camera motion. To achieve this, we first introduce adaptive CoNo-Splatting, a technique for fast and faithful rasterization that ensures efficient and effective handling of point clouds. Secondly, we integrate residual learning into the neural re-rendering pipeline, which improves convergence, generalization, and visual quality across diverse rendering tasks. Extensive experiments show that our method outperforms existing baselines with an improvement of **3~5dB** in rendered image quality, a reduction from **41 to 8** in GPU hours for training, and an increase from **3.6fps to 10fps** (our one-step variant) in rendering speed frequency.

Published as a conference paper at ICLR 2026
DIFFPBR: POINT-BASED RENDERING VIA SPATIAL- AWARE RESIDUAL DIFFUSION
Yiping Xie Yuchi Huo?, Yunlong Ran', Zijian Huang", Lincheng Li Yingfeng Chen?, Jiming Chen', Qi Yel.
'Group of Networked Sensing and Control, Zhejiang University, State Key Lab of CAD&CG, Zhejiang University,
Fuui AI Lab, Netease, Inc., Hangzhou, China, "Zhejiang Key Laboratory of Airspace Awareness and Autonomous Unmanned Systems
ABSTRACT
Neural radiance fields and 3D Gaussian splatting (3DGS) have significantly ad- vanced 3D reconstruction and novel view synthesis (NVS). Yet, achieving high- fidelity and view-consistent renderings directly from point per-scene optimization remains a core challenge. In this work, we present Diff- PBR, diffusion--asee framework that synthesizes coherent, photorealistic render- ings from diverse point cloud inputs. We demonstrate that diffusion models, when guided by viewpoint-projetted noise explicitly constrained by scene geometry and visibility, naturally enforce geometric consistency across camera motion. To achieve this, we first introduce adaptive CoNo Splattint a technique for fast and faithful rasterization that ensures efficient and effective handling of point clouds. Secondly, we integrate residual learning into the neural re-rendering pipeline, which improves convergence, generalization, and visual quality across diverse rendering tasks. Extensive experiments show that our method outperforms existing baselines with an improvement of 3-55 dB in rendered image quality, reduction from 41 to 8 in GPU hours for training, and increase from 3.6 FPS to FPS (our one-step variant) in rendering speed.
costly
INTRODUCTION
Rendering photorealistic images from colored point clouds is longstanding challenge in computer graphics, with significant applications in virtual reality, cinematography, robotics, and autonomous driving. In traditional point-based rendering pipelines, 3D point clouds are first projected onto 2D image planes through camera transformations, followed by z-buffer rasterization (Ravi et al. (020). While these techniques are efficient and well-integrated into conventional graphics workflows, they often suffer from artifacts like holes, aliasing, and surface discontinuities, which are induced by the sparsity of the point clouds.
To address these limitations, neural point-based graphics (neural PBG) approaches augment traditional rendering pipelines with learning-basee components, significantly improving visual quality and robustness. These approaches, such as NPBG (Aliev et al. (2220) and RPBG (Zhu et al. 2024). follow similar pipeline: starting with the triangulation of 3D points from multi-view where neural textures are initialized at each proxy point. These points are then rasterized onto the target view, followed by a CNN-based refinement module that reconstructs photorealistic images from the coarse renderings.
observations,
While these methods employ per-descriptor optimization using images captured from a single scene, they struggle to generalize across different scenes. To overcome this limitation, NPBG++ (Rakhimov ct al. 2022) introduces an online aggregation process that updates the neural descriptors of each point based input views, enhancing its cross-scene capabilities. Despite these advancements, rendering consecutive frames often results flickering, artifacts, and discontinuous jumps. To mitigate view inconsistencies, several approaches integrate alternative 3D representations (e.
"Corresoonding author.

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Published as a conference paper at ICLR 2026
CNNs in NPCR (Dii et al. 2020) and 3D Gaussians in PFGS (Wang et al. 2024a)) to alleviate the errors caused by splatting discrete point clouds onto image planes.
Given the improvements brought by combining multiple representations, fundamental question arises: is a point inherently a poor graphic primitive for rendering? With recent advancements in depth sensing technologies such as ofme-of-Flight (TOF) cameras, stereo depth cameras, LiDAR. monocular depth estimation ( (Yang et a 2024b), and 3D/4D reconstruction (Wang et al.) 2024b] Zhang et al. 2024, Chen al. 2025), point clouds have become prevalent modality alongside RGB images. Given the increasing availability of point clouds, can we achieve a purely point-based rendering pipeline without the need for integrating other representations? At first glance, the discrete nature of point clouds makes rasterization prone to artifacts such as holes and aliasing. This highlights the fundamental challenge of a purely point-based pipeline: mitigating rasterization artifacts while maintaining multi-view consistency.
In image restoration tasks, diffusion- iiffusiio-bbsed approaches have recently demonstrated impressive per- formance (Shi et al., 20224] Jiang et al., (2024, Xia et al.) [023 Zhu et al.) (023), offering strong generalization capabilities and high-quality outputs. These attributes make them a promising founda- tion for developing a generalizable point-cloud renderer. However, directly applying image-based diffusion models to refine point-basee renderings introduces several intrinsic challenges. First, the standard image restoration process typically operates per image, without considering viewpoint consistency. This leads to inconsistencies across different views of a scene, undermining the temporal and geometric coherence essential for multi-view rendering. These inconsistencies are particularly problematic when applied to point clouds, where the absence of explicit surface connectivity amplifies the sensitivity of rendering viewpoint changes. Second, diffusion models in image restoration rely on the assumption of pure noise inputs, yet degraded renderings of point clouds often retain substantial structural and color information. Reconstructing these inputs from scratch both unnecessary and computationally inefficient, as it requires recovering fine details that are already present in the scene. This inefficiency leads to excessive computational overhead, hindering the model's s performance. Third, point clouds lack explicit surface connectivity, making point-cloud rendering an ill-posed problem that is highly sensitive to woint-wise parameters such as scale. Poorly chosen parameters can lead to further artifacts and unreliable supervision signals, complicating the model's 's ability to generalize effectively.
To address these challenges, we propose DiffPBR, generalizable and view- niw-consssttent point- point-based based rendering framework composed of three complementary components. First, we replace the conven- tional i.i.d. Gaussian noise with view-aligned, geometry-aware noise maps generated via CoNo- Splatting. These structured noise maps encode geometric cues such as depth and occlusion, remaining consistent across viewpoints and enabling the diffusion model to produce spatially coherent outputs with minimal computational overhead. Second, we adopt residual diffusion paradigm, where the model predicts a weighted combination of residual (the difference between the rendered and ground- truth images) and noise. This formulation focus on recovering missing details, improving inference efficiency and enhancing generalization across diverse rendering conditions. Third, we introduce an adaptive point-based renderer that globally adjusts point scales, ensuring faithful projection while fully exploiting the diffusion model's capacity for refinement.
To summarize, we provide the following contributions:
We introduce novel and compact neural rendering pipeline that utilizes points the sole graphics primitives for rendering. It generates photorealistic and view-consistent images across wide range of scenes.
We propose a spatial-aware residual diffusion process to accelerate the training of lifting rasterized images to high-quality images and ensure consistent multi-view synthesis.
We propose an adaptive splatting strategy that dynamically adjusts point scales, ensuring faithful splatting and fully harnessing the capacity of the diffusion model.
Compared with the -fate-of-the-art, rendered image quality, a reduction from 41 to 8 in GPU hours for training, and an increase from 3.6 FPS to 10 FPS in the rendering speed frequency.
method demonstrates improvement of 3~55 dB in
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2
RELATED WORKS
2.1 SCENE SPECIFIC NOVEL VIEW SYNTHESIS
Novel view synthesis based on multi-view images for one scene has long been a fundamental challenge in computer vision and computer graphics (Zhang & Chen 2004). The evolution of this field benefits from continuous advancements in 3D scene representation. Voxel grids offer the advantage of representing arbitrary topological structures, and when combined with interpolation techniques. they can generate continuous representations (Upson & Keeler (988). Breakthroughs in neural volume reconstruction (Lombardi et al. (019) have revitalized research interest in this direction. NPBG (Aliev et al.) 2020) pioneered neural point-based rendering by first reconstructing point clouds and attaching teature descriptors to each point to encapsulate local geometric and appearance information. These points then rasterized into target views, enhancing point rasterization results through learned neural textures. The RPBG (Zhu et al. 2024) method follows a similar pipeline but employs surface-consistett neural descriptors and CNN refinement module to address temporal instability in point rendering. In contrast to explicit 3D representations using voxels or point clouds, recent advances in deep learning have driven significant progress in implicit representations (Xiang iet al. 2021 Kellnhofer et al. 2021). NeRF (Mildenhall et al. (021) achieves continuous mapping from spatial coordinates and viewing directions radiance values through MLP-based implicit functions, employing volume rendering to synthesize novel views. 3D Gaussian Splatting (Kerbl al. 2023) leverages differentiable ellipsoidal structures and modern CUDA ecosystems to achieve high-quality novel view synthesis in remarkably short timeframes. Despite the compelling quality of novel view synthesis with these graphic primitives, they are limited to scene-specific optimization. To achieve generalization to general scenes, these graphic primitives are incorporated into feedforward networks that are conditioned on input multiview images (Wiles et al. 2020 Wang et al. 2021 Yu
et al. a
2.2 GENERAL POINT-BASED RENDERING
The use of points as fundamental rendering primitives, known as point-based graphics, dates back to the 1970s (Csuri al. 1979; Levoy& & Whitted 1985). Points gained popularity due their storage flexibility, typically carrying 3D coordinates, colors, and optionally normals or radii, etc. In the early 2000s, Plister et al.) (2000) employed surfel splatting techniques blend overlapping regions, achieving high-quality and efficient point cloud rendering. However, traditional point cloud rendering methods exhibit severe artifacts and holes when processing sparse point clouds or handling inaccurate point predictions.
To address these limitations, researchers have integrated point cloud representation with deep learning approaches. NPBG++ (Rakhimov et al. (022) introduces online descriptor aggregation that updates point features from input views. These aggregated descriptors are rasterized onto 2D image plane and decoded into novel view images using a neural network. TriVol (Hu et al. 2023). on the other hand, proposes to combine point graphic primitives with anisotropic voxel grids fusion and neural radiance fields for image rendering (Mildenhall et al., 2021). PFGS (Wang et al.) 2024a) draws inspiration from 3DGS (Kerbl et al.) 2023) by representing points as adaptive Gaussians and employing differentiable splatting learn neural descriptors improve view consistency, A shared concept of these works is acquiring 3D descriptors for each point primitive, either by direct aggregation of color features from pre-trained feature extractors or finetune/learning the descriptors by multiview images, where other representations may be incorporated to help the learning.
Despite the great progress made by these works, we have found that splatting the descriptors tend to show blurry results as features from multiview images itself are inconsistent and direct aggregation can cause blurry effects. Although finetune/learning these descriptors from multiview images may help reduce the issue, the rasterization of the descriptors in discrete space still faces the inherent issues induced by the sparsity of point clouds. Unlike this paradigm, we work purely on the original point space and its original color values. Our work introduces iiffussionbbase framework with residual refinement of rasterized images, and we address the view consistency issue by injecting structural diffusion noise in 3D space whose attributes are adaptively adjusted with the point cloud density. This point-based rendering pipeline offers improved view consistency, convergence, and rendering quality without explicit temporal supervision.
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(1) Adsyttis Coll-Spbrting
Spottsiasers Renidial Diflccinn
Trakiing
Diffution L-Nett
Tageevey
Camera Trajedory
ENC
DEC
Spattal-eare Rasidual Diftcioon
Inference
Oihhaiion U-Met
ENC DEC
1P
Figure Framework Overview. Given colored point cloud and calibrated cameras, DifIPBR synthesizes photo-realistic renderings via Adaptive CoNo-Splatting (Sec. 3.T] for view-consistent initialization, and Spatial-aware Residual Diffusion stage (Sec. 3.2) for refinmmnt.
METHOD
Given point cloud p [(x;, c;) = 1,2,...,,Ai coordinates and color values of the points, we aim to synthesize photorealistic and view-consistent images from arbitrary camera perspectives.
n), where x; R and G; RR3 represent the 3D
While direct point-based rendering can produce plausible projections, the discrete nature of point clouds often leads artifacts and inconsistent details across views. Inspired by recent advances in generative modeling (Shi et al. 2024] Jiang et al. 2024| Xia et al. 2023) Zhu et al. (023), we leverage a diffusion model remove rendering artifacts from point-based rasterization. However. standard diffusion models, such as Denoising Diffusion Probabilistic Models (DDPM) (Ho et al. 2020), transform Gaussian noise into clean data via iterative denoising. Despite their success in generation, they are often suboptimal for restoration tasks where the input already contains meaningful structure. such cases, reconstructing the entire image from pure noise inefficient and unnecessary, Moreover, the inherent stochasticity of the diffusion process often leads to poor temporal multi- view consistency: since each sampling trajectory is initialized from random noise, predictions across adjacent viewpoints can diverge, causing noticeable inconsistency and temporal flickerigg.
To address these challenges, we harness 2D diffusion models for point-based rendering to improve both fidelity and cross- view consistency. Specifically, we propose spatial-awwe -aware residual diffusion module (RDDM) with 3D-consistent noise guidance for coherent multi-view outputs. Based on RDDM, further develop DifIPBR, renderer consisting of main components: (i) adaptive point-based rasterizer that produces paired color renderings Ia, noise renderings Ie, and soft mask Im, and (ii) spatial-awe awarre residual diffusion module (-) that enhances visual fidelity by recovering hhgh--ffegg nigh-frequency details while enforcing cross-vies consistency. An overview of the complete pipeline provided in Fig.
3.1 ADAPTIVE CONO-SPLATTIOE
In the first stage, we construct adaptive point cloud P... [(xi, C₁ ii= n) for appearance and noise rendering, respectively. Each point is initialized with a zero-mean Gaussian noise vector E 3 and isotropic scale factor 8; (1, R3 that characterizes its contribution to the rendered pixels.
Let Kc; denote the k-nearest neighbors of x;. The mean distance to KG; is first computed and used as the heuristic scale 8;-
|xi X2112, XjEK,
E1
s; clamp_mant (5;, 3 median ((3,)a (3,ر)
where adaptively clamp the local distance to avoid excessively large values while preserving target-view consistency, using a global bound defined by the product of a learnable hyperparameter and the median of all local distances.
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(2] Ground Truth Image
(b)
Faitiruul Projection (Proper
Camera Ray
Surticn
Splattng
(Beroe Spacel
Masis
Valid
Irage Pleals
Cemera Curter
Splated Ploke
Empty Piznte
(c] Over-Scaled Projection (Large B)
(d) Under Scaled Projection (Small B)
Valid Mask
Maak
Sptemedd Plasks
Emppy Poontl
Spluttd Plecl
Enply Plach
Figure 2: Illustration of how point scale influences the distribution of valid pixels and holes in the splatted image. (a) Ground-truth image with dense pixel coverage over the entire screen space. (b) With sparse points, the splatted image leaves empty regions, yet pixels within the valid mask remain well aligned with the ground truth. (c) Excessively large point scales cause splatting artifacts, thus penalizing Lemp- (d) Excessively small point scales result in screen holes and bleeding problems, thus penalizing Leov.
Balancing efficiency and informativeness, design is simpler than previous point-based renderers. For example, PFGS requires a memory- and computation-intensiv CNN-based module for per-point scale prediction, with a carefully crafted two-stage training strategy. This heavy design constrains their scalability and generalization ability. Conversely, our goal for scale initialization is not to directly generate photo-realistic renderings. Since the point cloud is inherently sparse and discrete representation, missing regions cannot be recovered merely by enlarging splat sizes, which would instead introduce implausible artifacts. Rather, as depicted in Fig. 2 our learnable global scale multiplier| /3 is designed to balance faithful information preservation with the avoidance of spurious artifacts during the splatting stage, ensuring that the splatted images provide effective input for the subsequent diffusion stage.
To achieve this goal, we first employ differentiable point-based rasterization strategy that en- ables faithful projection of 3D points onto the image plane. We denote this rendering process as CoNo-Splatting, where each point, equipped with its color c; and sampled noise 4, is splatted onto the image plane circular footprint, producing the rendered RGB image Iğ and noise map 7 target view w:
T 1 CNSplat (P... K" M.M.
Specifically, given the camera intrinsic matrix K and extrinsic matrix M the forward splatting step follows general point- based rasterization formulation:
En A (p n (K" M x,))/8, v(2)
F(P) = Σ₁=1 {(pp (K* M x,))/s}) v(2g)) +
Vpen.
(3)
Here, p denotes a pixel location on the feature plane Q R 7(-) () is the standard pinhole projection mapping a 3D point to its 2D location; x(-) is a differentiable splatting kernel distributing each point's s contribution to nearby pixels; 2, the depth of point the camera coordinate frame; v(.) is generic differentiable visibility weighting function; and > 0 ensures numerical stability.
From the rendered feature map F()), the Ie and I are extracted as:
T (p) (F()) (p) (F())440. VpEΩ.

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Published as a conference paper at ICLR 2026
Algorithm 1 Multi-step DiftPBR-Q Q Training
Algorithm 2 Multiistep DiftPBR Q Inference
Inputs: views V {(Tū, K", "M"),"" point
Inputs: cameras C === {(K", h=))=, point
cloud P. = {(xx, c(),, c;)]
cloud P. (x)x
Hyperparameters: training steps K, diffuusion
Hyperparameters:: diffusion steps T
steps T. learning rate v
Р. Initialization(P))
P. InitializatinnP.) Pe)
T argmin/_1 Va.
T" arg min? Vä.
for L....., do I CCN K" M))
for l...., K for v = 1,..., do
7
I. ====a] =a(T²)
sample U(1 Ie, 1- CNNSpplt K" M))
IT. CT I dr. I.
for = T 1..1000 do
Im a(1"), Lens f(l 1 18,
1
Fo 12 t,)
I 1. +1 Tz) 1 Pr
11 end for end for
rese I" 7//
Luw |ress Fo Ie, Im; l)ll2 Ve, Ldma
13 Return: renderings
Jm= {
8.3 =
end for
Note: we simplify terms v Tt, vI ps. (1-vār) nt, and 22
15 end
I. respectively.
Next, we process the rendered results and generate soft mask I by marking empty image regions as invalid. Let In = a(Ic), where (--) extracts the per- per-pixel opacity from the rendered I"" A discrete spatial distribution is then defined as:
Th (i, p(i,5) En, I (1, +
(5)
We then introduce two regularizers for optimizing 3:
Lenv ((G)))~ (00o-p[[(//6/ (l).)) - lo(i,
(6)
Lemp B(. log (p(i,j) 6)]-
As illustrated in Fig. Leov (coverage loss) encourages larger spatial coverage by diluting recon- struction errors over the valid region, while Cemp (compactness loss) discourages overly spread-out masks. Their weighted combination:
Lens = Aeov Leov + Xemp Lemp
(8)
Lemp alone would drive toward an overly small mask, while Loov counterbalances this tendency by promoting sufficient spatial extent. Together, the two terms jointly regulate 3 toward well-structured and stable solution.
establishes complementary interplay:
3.2 SPATIAL-AWARE RESIDUAL DIFFUSION PROCESS
Once the rendering triplets (Te, Ie, Im } are obtained, we integrate them into the training and inference stage of diffusion models. As deseribed Algorithm [ and Algorithm / the color image I, and the mask image Im are utilized as conditioning inputs to the diffusion model, where Ia preserves faithful appearance cues and I, enforces precise localization the missing regions. In addition, splatted noise map Ie, which embeds structural priors, is leveraged to construct the supervision target. predictor Fe then optimized by minimizing the discrepancy Lydm between the ground-trutt residual-noise rese and the model prediction, formulated
Pyim Elo. [[rrese Fo Ic. lm, 1)11
This design brings several key advantages:
(i) Residual learning improves generalization and efficiency. Rather than reconstructing images from pure Gaussian noise, the residual diffusion model predicts the discrepancy between rendered and ground-truth images. This simplifies the learning objective, the network only needs to recover missing high-frequent details or correct subtle distortions. Consequently, the learned representations generalize better across diverse scenes, while starting the reverse process from

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Table 1:
Quantitative evaluation of state-of-the tfettrrrtttttt point-based rendering methods on three benchmark datasets. f indicates our reproduction results of the method.
ScanNet
DTU
THuman2.0
Method
PSNRT
SSIM1
LPIPS.
PSNRT
SSIMI
LPIPS
PSNR
SSIMT
LPIPS
Pytorch3D
13.62
0.528
0.779
12.15
0.525
0.682
20.26
0,905
0.337
NPBG
15.09
0.592
0.625
13.52
0.703
0.514
19.77
0.915
0.112
NPBG++
16.81
0.671
0.585
22.32
0.833
0.327
26.81
0,952
0.062
TriVol
18.56
0.734
0.473
19.25
0.592
0.518
25.97
0.935
0.059
PFGS
19.86
0.758
0.452
25.44
0.901
0.164
34/74/35.88'
0.983/0.985*
0.009/0.006'
DiffPRRE
22.92
0.816
0.412
28.15
0.919
0.138
40.89
0.985
0.006
DifIPBR-Q
23.28
0.827
0.399
28.45
0.935
0.124
41.27
0.989
0.003
informative renderings- rather than random noise luise-substantially reduces the number of denoising steps, accelerating inference without quality loss.
(ii) Structured noise maps encode geometric priors. The noise rendered via CoNo-Splatting replaces i.i.d. Gaussian noise, implicitly embedding geometry: pixels receive stronger contributions from points closer to the camera, while distant or occluded points contribute less or are suppressed. Since the model predicts the residual noise, it must decode these geometric cues, thereby acquiring spatial awareness without explicit depth supervision.
(iii) Noise consistency ensures coherent generation. Because the rendered noise maps originate from the same 3D point cloud, they naturally preserve geometric consistency across viewpoints and time steps. During inference, this consistency serves stable guidance signal, maintaining coherence along camera trajectories. This is especially beneficial in video-based or multi-view synthesis, where standard diffusion models often suffer from frame-to-frame inconsistency due to stochastic noise initialization.
In summary, our design tightly couples point cloud rendering with diffusion training, allowing us to inject geometric priors directly into the generative process and improve both quality and consistency of the outputs. Please see Sec. Af the supplementary materials for further details of 3D consistent I
4 EXPERIMENT
4.1
IMPLEMENTATION DETAILS
Baselines, Datasets & Metrics. We employ traditional graphics renderer Pytorch3I (Ravi et al. 2020)) two point-based novel view synthesis methods NPBG (Aliev et al. 2020) and NPBG+ (Rakhi- mov et al. 2022), as well as NeRF-based renderer TriVol (Hu et al. 2023) and Gaussian-based renderer PFGS (Wang al. 2024a) for comparison. Following PFGS, we evaluate DifiPBR on three datasets: scene-level indoor dataset ScanNet (Dai al. 201 an object-level dataset DTU (Jensen et al. 2014), and human body dataset THuman2.0 (Yu et al.] (00Tb)) respectively. To assess recon- struction quality, employ PSNR, SSIM (Wang et al. 2004), and
Value
0.6
0.4
fres Eew
Eres Eow
LPIPS (Zhang et al. 2018).
Step
Training & Inference. DifiPBR is trained in an end-to-end manner without stage-wise pretraining, using 8 NVIDIA GeForce RTX 3090 GPUs, while inference is performed images are randomly cropped to 256 x 256, while the resolution is preserved during inference.
Figure 3: Adaptive 3 value under different loss combina- tions.
single GPU. The training

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Published as a conference paper at ICLR 2026
Reference
DIMPER (Ours)
PFGS
NP8G
TriVol
ScanNet
DTU
THuman2
Figure 4: Qualitative Results. We show comparisons of ours to previous methods and the corre- sponding ground truth images from held-out test views.
Model Variants. Catering to different needs, we provide two versions of DifiPBR: DimPBR-Q (Quality) with five sampling steps and DiftPBR-E (Efficiency) with a single-step sampling. A more detailed discussion is provided in Sec. B of the supplementary materials.
4.2 COMPARISON WITH BASELINES
Evaluation of Rendering Quality. To validate our method, we evaluate it against strong baselines on three challenging datasets. Quantitative comparisons reported in Tab. [ where the best and second best results are highlighted in bold and underlined, respectively, while qualitative results are shown in Fig.
Table 2: Evaluation of Model Efficiency on Thu- man2.0.
As shown in Tab. our method consistently surpasses all competitors, with only marginal differences between the one-step and five-step variants. In Fig. 4 TriVol suffers from severe artifacts, while NPBG++ and PFGS better aggre- gate point features but at the cost of excessive smoothing that suppresses fine details. In con- trast, our DifiPBR-Q combines point-basse ren- dering with residual diffusion, yielding sharper and more realistic outputs. By implicitly modeling failure patterns in the rendered inputs and adaptively correcting them, it preserves pixel-level fidelity while enhancing generalization across datasets.
Method
Training
Inference
PSNR
PFGS
41
3.6
35.88
DiA
8
10
40.89
DitPBR--
8
2
41.27
Evaluation of Model Efficiency. Tab. 2 reports rendering quality (PSNR) and cestrincyy-masuure as training time (GPU hours) and inference speed (FPS) for DifIBB and PFGS on THuman2.0 with 80K points using NVIDIA RTX 3090 GPU. Results show that both DiffPBR variants converge faster and yield higher rendering quality than PFGS, with the one-step version achieving about 3x faster inference.
Robustness with respect to Point Density. We evaluate the robustness of our method under varying point densities on Thuman2.0. Training point clouds randomly downsampled with ratios from 0.5
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Reference
Thuman2.0 (Ours)
DTU (Ours)
Thuman2.00 (PFGS)
DTU (PFGS)
Figure 5: Cross-dataset generalization. Evaluation results labeled "DTU (X)" indicate that method is trained on DTU without fine-tuning on Thuman2.0, whereas "Thuman2.0 (X)" refers to the in-domain setting.
to 1.0, while testing uses fixed ratios (1/2, 3/4, 5/6, 1.0). As shown in Tab. 3 our method consistently improves with higher input density and substantially outperforms PFGS.
Table Robustness with respect to point density on Thuman2.0.
Evaluation of Cross-dataset Generaliza- tion. To assess generalization, we directly evaluate our method and PFGS on Thuman2.0 using models trained on DTU. As shown in Fig. 5 our approach preserves finer details, whereas PFGS depends on a time-consuming pretraining stage to learn point cloud priors for Gaussian property prediction, which re- stricts its cross-dataset generalization. In con- trast, by leveraging residual diffusion, our method can be trained on images with diverse degradations and achieves stronger generalization unseen datasets.
Training
60k
120k
Inference
60k
80k
100k
120k
NPBG++
26.34
27.12
27.33
27.55
TriVol
25.93
26.21
26.71
27.08
PFGS
36.16
36.45
36.58
36.59
DifiPBR-E
39.87
40.96
41.29
41.72
DifIBR-Q
40.02
41.29
41.62
42.01
4.3 ABLATION STUDIES
Table 4: Efficiency of Adaptive Splatting on Scan- Net. We report both GPU runtimes and memory for processing 100k points.
Efficiency of Adaptive Splatting. As de- scribed in Sec. we initialize point scales with a heuristic KNN-based strategy, fol- lowed by an adaptive global scale regular- ization. For comparison, we implement two learnable alternatives based on Ioo-Multi-Lyyee Perceptron (MIP) and 3D CNN-for scale prediction. Tab. presents the GPU run- times and memory for processing 100k points, measured in milliseconds (ms) and gigabytes (GB), respectively. Our method directly computes scales without network inference, achieving nearly 100x speedup over MLP and 20 over CNN, while requiring considerably less GPU memory.
Method
Memory
Runtime
(a) KNN-based scale calc.
0.094
5.35
(a) (b) adaptive scale reg.
0.097
5,50
(c) ML.--aaed scale pred.
10.32
525
(d) CNN-based scale pred.
0.911
120
Table Effeet of Adaptive Splatting on ScanNet. PSNR of rendered RGB images after CoNo-Splatting and diffusion refinement.
Effect of Adaptive Splatting. Following prior works, we pretrain both MLP- and CNN- based scale predictors. As shown in Tab. 5 predictors with priors yield splatted images closer to the ground truth (e.g., splatted im- ages with CNN-predicted scales reach an av- erage PSNR of 19 19.42). However, such strong inputs may become double-edged sword for diffusion learning, contributing merely 1.66 dB PSNR gain in refinement. In contrast, weaker initializations (e-g., KNN-based scales) encourage the model to learn richer geometry and tex- tures under stronger gradients, while the dynamic scale constraint further promotes better parameter selection during optimization.
Method
Splatting
Refinement
(a) KNN-based scale cale.
13.43
22.05
(a) + (b) adaptive scale reg.
15.14
23.28
(c) ML.--based scale pred.
18.77
21.47
(d) CNN based scale pred.
19.42
21.08
Table Analysis of Adaptive Scale Regularizers
Analysis of Adaptive Scale Regularizers. As shown in Fig. 3 and Tab, 6 optimizing 8 with diffusion loss alone is inefficient, yield- ing only a 0.26 dB improvement over direct KNN initialization (row 7). Incorporating
w/o Lens
w/o Leos
w/o Lemp
Full
PSNR
22.31
22.69
22.97
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Cemp degrades 3 and, within moderate range, introduces holes shown in Fig. b). This in turn drive the diffusion model to capture finer details. Conversely, Leor encourages expansion and prevents the splatted images from collapsing Fig. Lid. Their adversarial balance is essential for achieving stable convergence and high-quality rendering.
Effect of Modules in Residual Diffusion, As shown in Tab. 7 we compare diffusion strate- gies (DDPM vs. RDDM) and noise types (2D random vs. 3D consistent noise). RDDM not only improves PSNR by an average of 2 dB but also converges significantly faster, requir- ing only sampling steps compared 50+ for DDPM. Incorporating 3ID-consistent noise further accelerates convergence while providing an additional 0.75 dB gain PSNR by injecting structural priors from 3D space.
Table 7: Effect of Modules in Residual Diffusion.
Method
PSNR
Cony,
DDPM 2D random noise
19.22
104k
DDPM 3D consistent noise
20.07
92k
RDDM + 2D random noise
21.54
44k
RDDM 3D consistent noise
22.15
37k
CONCLUSION
In this work, present diffusion-based framework for point cloud rendering that ensures temporal and spatial consistency via structured noise and residual denoising. By combining standard diffusion objectives with geometry-aware noise, our method naturally handles viewpoint variations without handerafted temporal constraints. To our knowledge, this is the first application of residual diffusion in point cloud rendering, enhancing photorealism by refining noisy renderings toward realistic outputs.
ACKNOWLEDGEMENTS
This work was supported in part by NSFC under No. 6.233022,,,,,, 62293511, Key Research and Devel- opment Program of Zhejiang Province under No. 2025C01066 Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China under No. JYB2025XDXM103, CCF-NetEase ThunderFire Innovation Research Funding under No. 202305.
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APPENDIX
The contents of this appendix include:
Formal Definition and Further Elaboration of Symbols
Details of DifiPBR Variants
Implementation Details
System Runtime Analysis
Additional Experiments
Additional Related Works
Limitations and Future Directions
Full Evaluation Metrics
A FORMAL DEFINITION AND FURTHER ELABORATION OF SYMBOLS
Tab. provides formal definitions that ground the main paper and Appendix. Be- low, we further detail several key concepts.
Table 8: Summary of the important symbol definitions.
Symbol
Definition
For each point, we introduce a 3D noise vector 6,, sampled ii.dd from a Gaussian distribution rather than inferred from spatial neighbors. This vector is concatenated with the point's color attributes and splatted once into the target view, yielding six-channel feature map composed of RGB and noise. The noise vectors initialized single time before rendering and remain fixed there- after. As shown in Fig. 6 the resulting noise maps exhibit consistent variations across viewpoints while implicitly encod- ing geometric cues such as depth and oc- clusion, thereby providing stable and infor- mative guidance for the diffusion training and inference.
Noise attribute
position attribute of the i-th point color attribute of the i-th point noise attribute the i-th point k-nearest neighbors of the i-th point heuristic scalar scale of the i-th point calculated by KNN
64
IC
8
learnable global scale regularizer adaptive scalar scale truncated by /
i-th point
isotropic scale vector of the i-th point point cloud with only position and color point cloud extended with adaptive scale I.d. 8, and 3D noise e, ~ (O 1)
Pin
index of camera perspective V E [1, m]
index of diffusion step [0, 7")
total diffusion steps truncated diffusion steps
K
3 3 intrinsic matrix of camera view
M
4 4 extrinsic matrix of camera view rendered consistent noise view " rendered RGB image view ground-truth RGB image at view residual image defined as I - 1" soft mask of 1 at view intermediate RGB image at view and step output refined RGB image at view variance schedule at step f signal preservation coefficient at step cumulative product o, up step compact notation defined as // di compact notation defined as vā. compact notation defined as /a.) diffusion U-Net backbone
Global scale regularizer 3. After KNN- based hcuristic initialization per-point
7
T
scales, we introduce learnable scale reg- ularizer 3 that truncates point scales ex- ceeding global upper bound. Instead of per-point scale regularization-whine curs substantial training and inference over- head and scales poorly with larger point clouds-ow our single global parameter pro- vides an efficient and adaptive mechanism for scale control. By jointly optimizing / within the diffusion training process, the model learns to automatically calibrates point scales that coherently align with the denoising objective, thereby eliminating the reliance pretrained CNN/MLP pri- ors and enabling more robust and adaptive training.
TO
L"
I
in-
18
8
Pe
"t
Fo
loss encouraging the expansion of 3 Lzou
Lemp
loss encouraging the contraction of
Lena
weighted sum of Loow and Lemp
Cam
loss measuring diffusion output accuracy
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Algorithm 3 One-step DiftPBR-E - Training
Algorithm 4 One-step DifiPBR-E Inference
Inputs: views V {(Tū, K", -",
Inputs: cameras C == {("" k= point
cloud P. = {(xx, c(),, c;)]
cloud P. = {(x,, cxx, ]=]
Hyperparameters: training steps K, difffusion
Hyperparameters:: diffusion steps T
steps T. learning rate v
Р. Initialization(P))
P. Initialization(Pa. Pe)
T argmin/_1 Vā.
T" arg min? Va.
for 1....,, do I I. === SpNalla K" M))
for 1....,
6
for v === ma do
7
I = =a(I²)
7
1" 1-- = -CNSSplt(PP. no )))
IT. TTi Ie pr I." To Fo( To Tin T')
I- a(I"), Lens L(T 18 Im) m
H TTT. 111-+11-1p0 -TTr I pro 12
10 end
10
H = Fa(Liu, : Im.T""
11 Return: renderings { Jv
11
Lam Lreal)) Lipipa ()) Lyram
12
0.3 - Vo,a(Lens Ldm)
Note: we simplify terms vai TG, v1 āe pt. (1 pp Te. and 1 Ig = I. respectively.
end for
end for
Truncated Diffusion Steps 7", Given total diffusion Steps T, the truncated time step T defined as:
T' = arg min ==
Vā:- 2
(10)
Intuitively, the truncation strategy indicates that the reverse process of the residual diffusion model starts not from pure Gaussian noise (at step T), but from inniminformttive state that retains partial structure and semantics of the original image (at step T',, as illustrated in Fig.
Mathematicallly, such truncation can be viewed as smooth equivalence transformation of the diffusion process that retains its generative behavior while facilitating more efficient inference. Specifically, the redefined diffusion forward process be formalized as:
I = Va+ To + (1 - Var) I + vI
1
(11)
which can be then reparameterized as:
Te = Vo, Tj
Vo.) I + vI σt 1 = (2 - 1) 1 + (1 a 1 vI 11-a;
(12)
(13)
Note that T5 is unavailable during inference, making the direct computation of Tÿ intractable. Fortunately, at the truncated step TV, the weighting coefficient for Tÿ in the diffusion formulation can be approximated to zero (i.e.. 2 1 0) given a predefined linear scheduler. This allows 47. to be estimated as:
1r (1 - var) I + VI 1 ~ ar 1 + v1 δT 1"
(14)
(15)
Since I4. explicitly computable, we restrict diffusion steps S TV, while truncating the remaining steps to eliminate potential risks. The reverse process thus starts from T" and gradually fits the current estimate 1 to the ground truth fü implicitly reducing the residual term between Ig and I5-
3D consistent noise r.. Unlike standard 2D random noise, our 3D consistent 1 is rendered from the 3D point cloud, and therefore inherits two critical properties: (i) view consistency, noise patterns across different views remain mutually aligned due to their shared 3D origin; and (ii) geometry embedding, as the noise distribution is modulated by depth and point-scale variations, implicitly encoding structural cues.
Intuitively, the noise images rendered across multiple viewpoints in Fig. 6 demonstrate that IN preserves the coherence of cross-view and reveals the coarse geometry of the scene. To further assess the statistical properties of T compute the covariance of the produced noise, its cross-covariance
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Reference
(a) Ramdom
(b) Ours
(b) Random
(b) Ours
Distribtton
View
Covarfance CovE?!. 11
View
Cross Cross-Covarin Covertance Covit, R)*)
View
Figure 6: Illustration of 3D consistent. I" is rendered from 3D point cloud, ensuring multi-view consistency and geometric awareness while preserving i.i.d. Gaussian distribution properties.
with the noise of nearby viewpoint, and the distribution of the splatted noise values. The results show that random noise (a) does not show a correlation with nearby viewpoints, while our 3d consistent noise (b) preserves the Gaussian faithfully representing the noise correlation between viewpoints, yielding an ideal 3d consistent noise map and remaining computationally efficient.
-ncciiiii-innnnnnnn mean, variance, and i.i.d. nature nature-whil
B DETAILS OF DIFFPBR VARIANTS
As mentioned in the main paper, we propose two complementary variants of DifPPBR, tailored to different trade-offs between quality and efficiency. Specifically, DiffPBR-Q adopts a multi-step sampling strategy to maximize rendering fidelity, while DiffPBR-E employs single-step sampling scheme to prioritize efficiency.
As detailed in Sec. 3.2 of the main paper, the multi-step variant DifiPR-Q adopts residual diffusion pipeline, prioritizing reconstruction fidelity. To further enhance efficiency, we introduce DifiPBR-E one-step formulation. This variant is motivated by recent findings that diffusion models remain effective with only few iterations (Liu et al. 2024 Shi et al., (024)). and in some cases even a single step (He al. 2024; Paarmar al. 2024). We provide the training and inference of DiffPBR-E in Algorithm
3 and Algorithm
Rather than directly reducing the diffusion steps T' to retain the full multi-step noise schedule but skip the intermediate steps during both training and inference. The resulting time step subset is defined as Ti {1, J" J. sub-sequence of the original schedule TM 21,
Notably, the one-step version is trained to directly predict the ground-truth images (i.e., x pr-prreictiion. allowing the training to be supervised by more informative signals rather than merely regressing the residual noise.
As described in Algorithm we supervise the training process with losses derived from readily available 2D supervision. The final loss includes the pixel-wise RGB L2 loss Cree between the predicted and ground-truth image, given by:
Lree 1
I l1,
(16)
the perceptual loss Lipips based on the VGG-19 features (Simonyan & Zisserman 2014), given by:
Lipips 7
Σ
[VVGG(()) VGG
(17)
E=
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and the Cram-matris (GM) loss grram images, given by:
(aatys et al. 2016) to enhance the sharpness of reconstructed
gra Σ or [GM)(T)) - GM)(T)))
(18)
where (+G E Hxxxx is the features from the I-th layer of a pre-trained VGG-19 network, L the number of layers considered, or is the weight of the I-th layer, and the Gram matrix layer / is defined as:
(1) (I)
(19)
During inference, the model operates in a non-iterative manner, yielding high-quality renderings without iterative sampling.
C IMPLEMENTATION DETAILS
C.I DATASETS
ScanNet is a large-scale indoor dataset comprising over 1,500 scenes and more than 2.5 million images, captured from different viewpoints and under varying quality conditions. We select the first 1,200 scenes for training and 300+ scenes for testing. During training, we aggregate the three nearest views to construct view frustum point cloud from RGB-D images and their corresponding camera poses. This strategy decouples our method from the original scene scale, enabling efficient training on large-scale and complex scenes. At test time, we utilize the entire preprocessed sparse point cloud input, ensuring consistency with the baseline methods.
DTU a large-scale dataset for multi-view stereo. It includes over 100 scans taken under seven different lighting conditions and camera trajectories. We partition the dataset into 88 training scenes and 16 testing scenes, using an image resolution of 512 x 640. For each scan, we input the whole point cloud, which is constructed from the RGB-D images and subsequently down-sampled by factor of 0.3 for sparsity.
THuman2.0 dataset consists of 500 high-quality human
captured using dense DSLR rig. We use the first 75% of the scans for training and the remaining scans for testing, with an image resolution of 512 512. For each scan, we densely sample le6 points on gt mesh and use Pytorch3D to render 36 views as ground truth. During training and testing, the point clouds are down-sampled to 80k for their sparsity.
C.2 MODEL ARCHITECTURE.
Our implementation of DifIPBR derives from Denoising Diffusion Probabilistic Model (Ho et al. 2020) in Pytorch, where the denoising module predicts pixel-level results. The core architecture U-Net-style encoder-decoder operating across four resolution scales. At each stage, time- conditioned ResNet blocks are combined with lightweight linear attention modules, enabling the model to efficiently capture both fine spatial details and temporal dynamics. A shared sinusoidal time embedding modulates all residual blocks, ensuring consistent and effective temporal conditioning throughout the network. The bottleneck incorporates full-attention mechanism to aggregate global context, while skip connections facilitate the preservation of high-freque geegueryy structures across scales.
C.3 TRAINING & INFERENCE.
To construct each training batch, we randomly sample camera views across all scenes ensure diversity. For each selected view, we retrieve the corresponding point cloud required in the CoNo- Splatting process. To improve rendering efficiency, we perform view-dependent filtering by excluding 3D points that fall outside the target camera frustum, thereby reducing unnecessary computation while preserving relevant geometry. The rendered RGB images, noise maps, and corresponding ground truth are randomly cropped to resolution of 256x256. To enhance the model's robustness to variations in lighting and viewpoint, we apply aggressive color augmentations coppnattiiinnssssiiiillllllll color jittering and grayscale conversion-as well independent image-space flipping each frame,
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within the same scene. The U-Net model is trained from scratch without incorporating any physical priors. The training loss is a weighted sum of the splatting and diffusion objectives:
Acou + Xemp + Lam
(20)
where Acou = 0.01 and Aemp = 0.1. The training process is performed on 8 NVIDIA RTX 3090 GPUs, requiring near GPU hours.
At inference, we load the entire point cloud and initialize per-point scale and noise attributes once, thereby guaranteeing view-consistent color image and noise map across different camera views. To maintain compatibility with the U-Net architecture and prevent spatial misalignment, the rendered images are center-cre ceoter-rroppd such that both height and width divisible by 16. For resolutions below IK, a single NVIDIA RTX 3090 GPU supports efficient inference, making the model suitable for rapid prototyping and real-world applications.
D SYSTEM RUNTIME ANALYSIS
As mentioned in the main paper, DifIPB begins with an adaptive CoNo-Splatiing pipeline, followed by a diffusion refinement process. Consequently, the system's 's runtime primarily depends three key factors: the number of points in the input point cloud, the resolution of the rendered image, and the number of function evaluations (NFE) during diffusion inference. We provide the detailed analysis in All results are tested on a single NVIDIA RTX 3090 GPU.
Fig. 7
Splatting Stage. The runtime of this stage consists of two components. First, we con- struct point cloud P... with extended point attributes. In Fig. 7 we report the initial- ization time under different point densities. Subsequently, the generated feature points are rasterized onto the camera planes, pro- ducing couple of images. The blue bars indicate the rasterization time under vary- ing point densities and image resolutions. Except for the low-cost 256x256 resolu- tion, the rasterization runtime remains rela- tively stable across different point densities at fixed resolutions. Conversely, when the resolution is fixed, the runtime increases slowly and approximately linearly with the number of points.
1827
245-m 550
142
620
11.3
31.
6od-m 290.7
28455
Calle-pp
20.5-
Solesing
300
2.5
100
0.5
2542266
6905112
12032726
1426x1002
imge Recotation (pUAKO
Figure 7: Runtime evaluation of our system in dif- ferent stages. The blue bars indicate the runtime of the Sola-Stta process, with progressively darker shades of blue representing increasing point cloud den- sity, while the orange bars denote the single-step for- ward inference time of residual diffusion (NFF=1).
Diffussinn Stage. The orange bars denote the single-step inference time of the U-Net at different image resolutions. The total runtime of this stage scales proportionally with the number of diffusion steps (NFE). Note that for resolutions of 1920x1080, diffusion inference on a single RTX 3090 encounters out-of out-of- memory (OOM) issues. In this case, the single-step inference times are extrapolated based on trends observed in lower-resolution settings.
E ADDITIONAL EXPERIMENTS
E. MORE ABLATION STUDIES.
Diffusion Configurations. As detailed in Appendix B our multi-step diffusion model adopts e-prediction, whereas the one-step variant employs predictiio.. The diffusion configura- tion conn-including the parameterization type (i.c., prediction target) and the number of diffusion steps-is critical, it not only defines the training objective but also impacts the inference procedure.
In this part, we conduct ablation studies on diffusion configurations by varying the prediction target (e 2o) and the sampling strategy (multi-step vs. single-step). as shown in Tab. 99| Across all training sizes, e-prediction with multi-step sampling consistently achieves the best PSNR, confirming its effectiveness in modeling fine-grained structures through progressive denoising. Within each
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(a) Strategy
(b) Strategy
(c) Point Perturbation
Noise
2=
3D Point
CoNo- Splatting
CoNo- Splatting
Noise-
Splatting
Color & Noise
Splatting
Color
0=1e-3
3e-3
Figure 8: Illustration of Point Cloud Initialization and Perturbation. (a) The color of each point concatenated with a randomly sampled noise vector and jointly splatted to the target view. (b) For each point, we additionally sample new position and assign a noise vector, after which the noise point cloud and the original color point cloud are splatted to the target view separately. (c) The clean point cloud is perturbed at different levels controlled by group, we observe that e-prediction benefits more from multi-step inference, while ro-preddctionn performs slightly better with one-step inference. We attribute this difference to their respective training objectives: e-prediction supervised purely with & loss on the noise, which aligns well with iterative denoising; in contrast, co-prrdiction incorporates perceptual losses such LPIPS and Gram loss addition to pixel-wise l,2 encouraging high appearance fidelity that can be better preserved under direct (one-step) decoding. These observations highlight the roles of denoising strategy and prediction target diffusion-based reconstruction.
Table Ablation studies on different combina- tions of diffusion configurations. Each configura- tion is evaluated under different training set sizes on both THuman2.0 and ScanNet datasets.
also observe that models trained on THuman2.0 achieve higher PSNR when trained smaller subsets compared to larger ones. We attribute this to the simplified and spatially consistent degradation patterns in THuman2.0, which make it easier for the model to overfit and achieve better pixel-level recon- struction. While this leads to improved quanti- tative fidelity, it may come at the cost gener- alization and robustness when applied to more diverse or complex scenarios.
Interestingly,
THuman2.0
ScanNet
Config.
Data
PSNR
Data
PSNR
6 M.
15k
41.27
150k
23.28
6 S.
15k
39.56
150k
20.52
M.
15k
40.00
150k
20.88
30 S.
15k
40.89
150k
22.92
To verify our hypothesis, conduct additional experiments on the ScanNet dataset, which presents significantly higher complexity due to varying lighting conditions, motion blur, and in- complete well as uneven point cloud sparsity. The results show that models trained on larger subsets consistently outperform those trained on smaller ones, highlighting the importance of data diversity for generalization.
M.
5k
41.76
50k
22.88
5k
40.15
50k
20.98
2p M.
5k
40.87
50k
21.65
+ S.
5k
41.03
50k
22.04
6 M.
1k
42.29
10k
21.92
c S.
1k
40.22
10k
20.09
2o M.
1k
40.94
10k
21.01
1k 41.46
10k
21.46
Note: we simplify the terms "e-prediction" as "c" "xo-prediction" as "zo". ", "Multi-step" as "M.", and "Single-step" as "S.". respectively.
Point Cloud Initialization in CoNo-Splatting As described in the main paper, we augment each point's color with a zero-mean Gaussian noise vector, resulting in noise point cloud P. that retains the original geometry but carries random appearance for subsequent noise rasterization.
Table 10: Initialization Strategy in CoNo-Splattin, with experiments conducting on Thuman2.0.
In this part, we further explore the noise gen- eration strategy where the position and color of each point are independently sampled from Gaussian distributions, aiming to assess how dif- ferent noise patterns impact the training of the residual diffusion model.
Initialization
Noise Pos.
Noise Col.
PSNR
Strategy
X
41.27
Strategy
40.02
As depicted in Fig. 8 in (a) we sample a three- channel Gaussian noise vector and concatenate with the point color. The combined feature is then
19

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Table 11: CoNo-Splatting Configurations. Evaluation metrics for different choices of Acov and
Aemp.
Aemp
0.01
0.1
1
Acoe
0
0.01
0.1
PSNRT
22.31
22.57
22.69
22.39
PSNR
22.31
23.01
22.97
22.62
SSIM1
0.804
0,815
0.821
0,806
SSIMT
0.804
0.826
0.824
0.810
LPIPS
0.424
0.412
0.406
0.417
LPIPS
0.424
0.402
0.409
0.419
splatted to the target view as a whole. In (b), each point is instead modeled as a local Gaussian distribution in 3D, with variance defined as the mean distance to its k nearest neighbors. To render point noise, a new position is sampled from this distribution to replace the original point, whereas the position for point color remains fixed for color splatting.
As shown in Fig. and Tab. 10 strategy consistently outperforms strategy 2. The strict alignment of color and noise achieved by the joint splatting process in (a) leads to faithful preservation of fine structures, In contrast, the random positional noise introduced in (b) disrupts this alignment, resulting in degraded local detail reconstruction, as evidenced by blurred finger structures (highlighted in red boxes).
CoNo-splatting Configurations. As described C.3 of the main paper, the training loss is weighted sum of the splatting and diffusion objectives: C Acoe Lcoe + Aemphemp + Ldm. where Acov = 0.01 and Aemp = 0.1. Here, we provide more detailed ablation study on this hyper-parameter choice. Specifically, we set Acov and Xemp to zero individually to examine their influence. The results are summarized Tab. [] Note that Lam is the primary objective of the diffusion model, while these two regularizers are complementary. We assign them relatively lower weights compared to Lam.
Table 12: Ablation on the statistical metric used for global truncation.
Moreover, we further ablate the choice of the statistical metric used for global trun- cation in Eq. 12 Specifically, we compare four options: the 25th percentile (Q1), the 50th percentile (median, Q2), the 75th per- centile (Q3), and the Mean. As shown in Tab. [2] the median (Q2) achieves slightly better performance than the other statistics.
Metric
Q1 (25%)
Q2 (50% Median)
Q3 (75%)
Mean
PSNRT
22.41
23.28
22.91
23.19
SSIMT
0.803
0.827
0.819
0.822
LPIPS
0.421
0.399
0.412
0.404
Note that both the median and mean are widely adopted in practice; however, the median is inherently more robust outliers and provides stronger suppression of extreme values, making better aligned with our global truncation strategy.
Effect of Spatial-Aware Residual Diffusion Module. To further validate the efficacy of our Spatial- Aware Residual Diffusion module, we conducted an ablation study by replacing it with two common alternative image synthesis paradigms: Nearest-Neighbor (NN) interpolation and learning- based image inpainting. As shown in Fig. 10 the inherent sparsity and uneven distribution of input points leave holes the initial rendered images, causing direct NN interpolation to produce blurry and indistinet results. We evaluated two learning-boo based inpainting strategies. Mask-guided models (e.g., Inpainting Cormama-Creatinn 2024)) struggled to fill these regions, their training focuses on small, dense missing areas, unlike the large, continuous holes our inputs. Text-to-image models (e.g.. GPT-4o (OpenAl] (024)) can generate photorealistic outputs from text, but may deviate from the original image, violating multi-view consistency across viewpoints.
SD-XL Inpainting (Podell et al., 2023). FLUX-Contrane
Table 13: Effect of Adaptive CoNo-Splatting as a Plug-and-Pla Module.
Effect of Adaptive CoNo-Splatting Plug-and-Play Module. As described in the paper, our contributions are tightly cou- pled from both theoretical and empirical perspectives, but they can also be applied to other methods. Specifically, we integrate our Adaptive CoNo-Splatting module into the baseline PFGS. Originally, PFGS ex- tracts point-wise features and predicts per-point parameters such as scale and opacity via a Gaussian attribute regressor, the predicted colors and features then rasterized for refinement. In our experi- ments, we replace the Gaussian regressor and its rendering pipeline with our adaptive point-based splatting module. As shown in Tab. 13 and Fig. 11 this substirution significantly improves both
Method
PSNR
SSIMI
LPIPS)
GPU lns]
FPSt
PFGS
19.86
0.758
0.452
5+36
3.6
P.GS+A.CNNS.
0.433
2+17
Note: A.C.N.S. denotes Adaptive CoNo-Splattin.
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Poin Cloud
Striegy (e) Notse
Stratgy (b Noise
Reterence
PINR
PISRR
Figure 9: Visualization of noise map and diffusion outputs. The point clouds are initialized with strategies. Red boxes highlight degraded local details under strategy (b).
(g)
Figure 10: Comparison of refinement module outputs. (a, b) Image and mask from Adaptive CoNo-Splatting. d) Inpainting results guided by (b) from FLLUX-Contronne--nee XI--Inpainting, respectively. (e) Nearest-Neighhor (NN) interpolation on the image. (f) Output from GPT-4o. (g) Output from our method.
and SD-
rendering quality and efficiency. Training and inference are faster thanks to the removal of convolu- tional layers the Gaussian regressor, and the gap between the two training stages in the original PFGS is reduced, yielding better overall performance. These results demonstrate the versatility and effectiveness of our adaptive splatting design beyond its original context.
E.2
MORE COMPARATIVE RESULTS.
Table 14: Robustness with respect to varying point cloud perturbations on Thuman2.0.
Robustness with respect to Point Pertur- bations. The results in Tab. [4 are eval- uated under different perturbation levels, controlled by a variance factor o applied to the clean point cloud P. Ours outperforms PFGS across all settings, achieving higher PSNR with notably lower variance.
Method
20-3
30-3
Variance
PEGS 33.80
33.09
32.14
1.89
DHiPBR.F 39.62
39.09
38.60
0.73
DiAPAR-Q
40.33
39.62
39.05
0.69
Table 15: Evaluation of quality-efficiency trade-off on the DTU, we report rendering quality, training time (GPU hours), and inference speed (FPS) image resolution 640 512.
System Runtime Comparison. To evalu- ate the quality-efficiency trade-off, we com- pare the training time and inference speed (FPS) across methods on the DTU dataset at resolution of 640 x 512. The com- petitors our comparison generally follow two-stage pipeline. In the first (fitting) stage, they extract information from the source imagee. For Tri Vol, this typically involves fitting the neural representation of the scene based NeRF. For NPBG++, it corresponds to running a feature extractor on the selected neighboring views. Methods that rely on geometric proxies, such PFGS, require the construction of 3D representation as part of this stage.
Method
PSNR
SSIM
LPIPS
GPU hrs J
FPS
NPRG+
0.327
TeVVo
20.02
0.674
0.483
<448
0.07
PEGS
25.44
0.901
0.164
<.40
DiMPER-E
28.15
0.919
0.138
<8
9.75
DiRPER-Q
28.45
0.935
0.124
~
1.9
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Reference
PFGS
PFGS+Adaptive CoNo-Splatting
Figure 11: Effect of Adaptive CoNo-Splatting as Plug-and-Plaa Module. Incorporating Adaptive CoNo-Splating leads to better preservation of local geometric structures, yielding more faithful reconstructions.
The time required for this process is included in our measurements to ensure fair comparison. This fitting stage is performed only once per scene, after which the models can render arbitrary novel views-i.e the second stage (rendering). We show a qualitative comparison between rendering quality and rendering speed (FPS) Tab. [5] The results show that DifPBB achieves the best rendering quality while maintaining favorable trade-off in rendering efficiency.
we further present qualitative results on THuman2.0, DTU and ScanNet. The results demonstrate that our method yields renderings that are more visually harmonious compared to competitors.
Rendering Quality Comparison with Generalizable Methods. In Fig. 15 and Fig. 16
Comparison with Per-scene Optimized Methods. Tab. 19 and Fig. 13 present quantitative and qualitative comparisons between DiftPBR and state-of of the art baselines, including two per-scene optimized approaches, 3DGS (Kerbl et al., 2023) and RPBG (Zhu et al. 2024). Notably, 3DGS regresses a set of Gaussian primitives, while RPBG learns neural point descriptors, both tailored separately for each training scene to achieve high-fidelity novel-view synthesis. In contrast, DiffPBR trained over a diverse corpus of scenes and generalizes to previously unseen environments without spene-specific optimization. The results show that our method consistently outperforms both com- petitors on ScanNet, where the images contain motion blur and varying illumination. And on DTU, RPBG exhibits degraded performance several scenes as shown in Fig. [3] On well-conditioned Thuman2 dataset, our performance is comparable to 3DGS and superior to RPBG. Note that due to the consuminn per-scene training required by 3DGS and RPBG, the results reported in Tab. 19 are not evaluated on the full test set used in Tab. [
Table 16: Evaluation on unbounded scenes on Tanks and Temples.
Evaluation on Unbounded scenes. To fur- ther evaluate our performance on more di- verse scenes, we conduct experiments on large scale unbounded scenes Tanks and Tem- ples (Knapitsch et al.) 2017). Specially, we com- pare with one of the generalizable method PFGS and two per-scene optimized method 3DGS and RPBG. We the off-the-shelf feed-forward regressor Pi3 (Wang et al.) 2025) to obtain the initial point clouds and corresponding camera poses from the input images. Tab, 16 and Fig. [4 present quantitative and qualitative comparisons. The results demonstrate that our method gets the best result compared to all other methods.
Method
Per-scene
PSNR
SSIM
LPIPS
3DGS
24.69
0,785
0,207
RPBG
22.39
0.735
0.225
PEGS
X 24.31
0.759
0.296
Ours
X
25.25
0.801
0.157
Table 17:
Bilateral comparison on dense and sparse point clouds from the Tanks and Temples Panther scene.
Moreover, to further demonstrate the effective- ness of our approach under sparse point cloud inputs, we replace the dense point clouds pro- duced by Pi3 with sparse COLMAP registra- tion points. Specifically, we provide DiffPBR and the NVS basclines (3DGS and RPBG) with the same sparse-view COLMAP reconstructions. The results on the Panther scene reported in Tab.
Method
PSNR
SSIM
3DGS
COPMAP
22.16
0.726
0.300
RPBG
COLMAP
22.30
0.746
0.272
Ours
COLMAP
22.97
0.783
0.126
3DGS
P13
22.68
0.754
0.214
RPBG
P13
22.93
0.780
0.181
Ours
P13
23.49
0.854
0.109
Comparison with Pure Graphics-based Ren- derer. In the main paper, we compare our method with PYTorc33D, which performs differentiable rasterization-base rendering. To further broaden the comparison scope, additionally include
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Reference
DiffBR
Pytorch3D
Mitsuba
Figure 12: Comparison with Pure Graphics- based Renderer on ScanNet and DTU. Both graphics- based renderers fail achieve photorealistic results due point sparsity and structural noise.
Table 19; Quantitative evaluation of per-scene optimized point-based rendering methods on three benchmark datasets. Note that 3DGS and RPBG are per-scene optimized while ours is generalizable.
ScanNet
DTU
THuman2.0
Method
PSNRT
SSIMI LPIPS,
PSNR*
SSIM
LPIPS
PSNRT
SSIM1
LPIPS
3DGS
31.29
0.896
0.219
24.92
0.871
0.191
41.57
0.989
0.004
RPBG
31.10
0.885
0.171
23.80
0.822
0.173
36.60
0.982
0.020
Ours
31.62
0.900
0.139
28.33
0.921
0.124
41.21
0.988
0.004
Mitsuba, a physically based ray-tracing renderer. Since Mitsuba simulates light transport through ray tracing, its illumination and shading are inherently inconsistent with rasterization-base pipelines such PyTorch3D and ours. Therefore, only provide qualitative comparisons for Mitsuba, as shown in Fig. 12 rather than quantitative metrics.
Table 18: Multi-view consistency evaluation on Scan- Net. A higher TSED score indicates better multi-view consistency.
Evaluation of Multi-view Consistency. To assess the view-consistency of our model, we additionally report the Thresh- olded Symmetric Epipolar Distance (TSED) metric (Yu al.) 2023). which measures the proportion of frame pairs that
T
Ours
PEGS
NPBG++
TriVol
3DGS
RPBG
0.2143
0.1837
0.1531
0.2347
0.2245
4
0.4796
0.4286
0.3776
0.3265
0.4694
0.4388
satisfy epipolar consistency constraints.
0.8776
0.7755
0.7143
0.6735
0.8571
0.8061
A smaller threshold T primarily captures temporal flickering or instability between adjacent frames, whereas larger T reflects the global consistency of synthesized views under significant camera pose variations.
As shown Tab. 18 our model consistently achieves higher TSED scores than all generalizable baselines and is comparable to even better than per-scene optimized approaches, demonstrating superior multi-view consistency novel view synthesis.
F ADDITIONAL RELATED WORKS
In this section, we discuss additional related works on 3D consistent generation.
Directly applying 2D diffusion models view-by-view often yields iieeddepnndent artifacts such as flicker, since independent noise trajectories produce uncorrelated stochasticity across viewpoints. To address this, recent work enforces 3D-consistent noise or consistency-avaat denoising. One family of approaches exchanges information across views during denoising: ConsistNet introduces lightweight multi-view consistency blocks that unproject multi-view features into global 3D volume and reproject consistent features back to each view to align parallel diffusion outputs (Yang et al. 2024a). Another class constructs structured synchronized noise: ConsistDreamer generates 3D)
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Reference
DiffPBR
3DGS
RPBG
ScanNet
DTU
THuman2.0 0
Figure 13: Qualitative Results. We show comparisons of ours to per-scene optimized methods and the corresponding ground truth images from held-out test views.
aware, structured noise and augments 2D diffusion with surrounding views and self-supervised consistency during training so that the stochastic component is correlated across views (Chen et al.) 2024). Relatedly, Geometry-Aware Score Distillation (GSD) formulates a 3D-consistent noising process and a
loss in the score-distillatee sampling (SDS) pipeline to reduce multiview gradient inconsistencies and improve geometry-ave aware text-to-3D optimization (Kwak et al., 2024). Complementarily, some methods design conditioning and training schemes pretrained 2D priors to produce coherent multi-view images from single input (e.g., 2ero123++) (Shi et al. 2023). For temporal problems, Equi VDM demonstrates that using temporally consistent (warped) noise encourages equivariance and yields temporally coherent video diffusion outputs, suggesting an analogous benefit for multi-view/3D consistency when noise is correlated across frames views (Luu & Vahdat 2025). Finally, SyncNoise directly predicts geometry conssstet noise fields and leverages anchor-view propagation and depth supervision to further improve multi-vier consistency for 3D editing (Li et al., 2024). While these strategies substantially reduce view-dependent artifacts, many trade off computational cost (per-scene finetuning or added modules). In contrast, our approach derives 3D-consistent noise guidance directly from the point cloud via adaptive CoNo-Splatting enabling efficient residual 2D diffusion refinement without costly scene-level optimization.
G LIMITATIONS AND FUTURE DIRECTIONS
We present DimPBRR general residual diffusion framework for efficient and view-consistent refinement of point-based renderings with 3D-consistent noise guidance. A limitation of our method arises when large portion of the point cloud is missing, since the model is currently trained from scratch and lacks strong image priors to hallucinate unseen regions, Another practical limitation is that the current implementation's 's rendering FPS is lower than real-time rates, primarily limited by the output resolution. Thanks to its flexible design, DifiPBR offers a scalable foundation that can benefit from larger model capacities, richer training datasets, and more diverse point- point-cloud sources such as LiDAR scans, depth projections, or mesh-based samples. We envision it as a step toward a generalizable and robust point-based renderer.
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Panther
Ignatius
Family
Horse
Reference
DiffPBR
PFGS
3DGS
RPBG
Figure 14: Qualitative Results. We show comparisons of ours to per-scene optimized methods and the corresponding ground truth images from held-out test views.
H FULL EVALUATION METRICS
We provide the full of evaluation metrics here for the ablation studies and other experiments that previously reported only PSNR.
Table 20: Evaluation metrics for Tab,
Method
60k
80k
100k
120k
NPBG+
26.34/0.951/0003
27.12/0.955/0.05
27.33/0.957/0.053
37.55/0.958/0.00
TriVol
0/330.933300059
26.21/0.935/0.054
26.71/0.938/0.00
27.08/0.9399 939/0.049
PFGS
36/16/0.
(116//.98//.000 984/0.008
36.45/0.984/0.000
36.58/0.
00.58/0.985/0.00 085/0.006
36.59/0. 987/0.005
36.59/0.887/0.00
DifiPBR-E
39.87/0.983/0.000 983/0.007
N0.96/0.985/0.00
41.29/0.987/0.00 987/0.00044
41.72/0.988/0.00 988/0.003
DIAIBB--Q Q
40.02/0.985/0.000
41.29/0.989/0.000
41.62/0.990/0.00
02.01/0.990/0.000
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Published as a conference paper at ICLR 2026
Table 21: Evaluation metrics for Tab.
Tab.
and Tab.
Tab.
Tab. 6
Tabl. 7
PSNR
SSIM
LPIPS
PSNR
SSIM
L.IPS
PSNR
SSIM
LPIPS
13.43/22.05
0.519/0.805
0.782/0.422
22.31
0.809
0,418
19.22
0.742
0.468
15.14/23.28
0.602/0.827
0.633/0.399
22.69
0.815
0.414
20.07
0.783
0.447
18.77/21.47
0.735/0.796
0.470/0.428
22.97
0.820
0.404
21.54
0.796
0.425
19.42/21.08
0.740/0.782
0.466/0.433
23.28
0.827
0.399
22.15
0.808
0.420
Table 22: Evaluation metries for Tab.
Thuman2.0
ScanNet
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
41.27
0.989
0.003
23.28
0.827
0.399
39.56
0.985
0.011
20.52
0.790
0.436
40.00
0.986
0.009
20.88
0.793
0.428
40.89
0.987
0.006
22.92
0.824
0.400
41.76
0.989
0.004
22.88
0.823
0.405
40.15
0.986
0.009
20.98
0.796
0.423
40.87
0.987
0.006
21.65
0.801
0.419
41.03
0.988
0.005
22.04
0.806
0.419
42.29
0.990
0.003
21.92
0.804
0,419
40.22
0.986
0.009
20.09
0.783
0.449
40.94
0.987
0,005
21.01
0.796
0.422
41,46
0.989
0.004
21.46
0.797
0.421
Table 23: Evaluation metrics for Tab. 10 and Tab.
14
Table 10
Table 11
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
41.27
0.988
0.004
33.80
0.973
0.016
40.02
0.985
0.010
39.62
0.986
0,010
40.33
0.987
0.007
33.09
0.971
0.025
39.09
0.985
0.012
39.62
0.986
0.010
32.
0.967
0.031
38.60
0.984
0.014
39.05
0.985
0.012
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Published as a conference paper at ICLR 2026
GT
Ours
PFGS
NPBG+
TriVol
Figure 15: Additional qualitative comparisons between ours and baselines on Thuman2.0.
27

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Published as a conference paper at ICLR 2026
Reference
DiffPBR (Ours)
PFGS
NPBG+
TriVol
UE
Figure 16: Additional qualitative comparisons between our method and baselines on DTU (upper
block) and ScanNet (lower block).
28


Reviews:
[{"review_id": "U4EO9wI67k", "reviewer": "Reviewer_PDkh", "summary": "This work presents a method for rendering 3D point clouds to images. Specifically it aims for photorealistic rendering even from sparse point-clouds. This is achieved by using a customised diffusion model to \"tidy up\" the sparse rendering. To ensure the diffusion results are approximately multi-view consistent, noise is first sampled supported on the point-cloud, then rasterised to different viewpoints. The model then predicts the residual to map the sparse point cloud rendering to a realistic image. On three datasets of point-clouds paired with ground-truth images, the method is shown to achieve higher-fidelity rendering than selected baselines.", "questions": "See the points discussed in weaknesses above – in particular the missing baselines / ablations.\n\nL60 \"diffusion models in image restoration rely on the assumption of pure noise inputs\" – this is poorly worded, they typically input both a noisy image (as conditioning for the diffusion process) and latent noise that will be conditionally decoded to the final image; thus they do not receive *only* pure noise. Moreover, it is common in image restoration to start the denoising process at a DDIM-inverted or noised version of the input image, for exactly the reasons discussed in this paragraph.\n\nPlease provide LPIPS & SSIM for the ablation and other experiments that only use PSNR – LPIPS in particular is much more reflective of perceived quality.\n\nMany \\citet or \\cite should become \\citep", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 1, "strengths": "The proposed pipeline is novel – the combination of size-adaptive rasterisation strategy with diffusion-based refinement using spatially (multi-view) consistent noise has not been tried before.\n\nThe approach to arranging 3D-consistent diffusion noise is simple and elegant; similarly predicting residuals instead of directly reconstructing images is a sensible choice.\n\nThe main experiments on DTU, ScanNet and THuman2 show that the proposed method achieves higher rendering fidelity than Neural Point-Based Graphics, its more-recent extension NPBG++, TriVol (based on density fields), and PFGS (based on gaussian splatting). This holds across all three datasets, according to PSNR, SSIM and LPIPS metrics.\n\nThe method is shown to be significantly faster (for training and inference) than PFGS, and also more robust to point density (i.e. it retains higher fidelity even when the point cloud becomes sparser).\n\nThere are additional experiments on cross-domain generalisation, using the fairly disparate domains of Thuman2.0 people and the DTU reconstruction dataset. Even between these very different domains, the method achieves respectable visual fidelity when trained on images from one and validated on images from the other.\n\nThere is a fairly thorough ablation study measuring the benefit of various design decisions and components of the proposed method. Most notably, the 3D-consistent noise shows a significant improvement in PSNR compared with 2D noise.\n\nThe paper is clear, well-structured, and pleasant to read.", "weaknesses": "The technical contribution is fairly small – diffusion with 3D-consistent noise is now fairly well-established in other tasks such as multi-view-consistent novel view synthesis. Beyond this, the task of rendering sparse point-cloud realistically has been studied by several methods (including the baselines used in this work), and the additional insight given by the present work is fairly small. Similarly the \"CoNo Splatting\" described in Sec 3.1 is a trivial strategy for rendering point-clouds, accompanied by a simple pair of regularisers to ensure the points are a sensible size.\n\nThe text implies that the model guarantees 3D consistency across viewpoints; however while there is a strong encouragement to do so by the 3D representation of noise, it is not guaranteed since the diffusion itself occurs in 2D pixel space.\n\nThere is no comparison against naive baselines like nearest-neighbour interpolation of colors into empty pixels in 2D image space, or off-the-shelf diffusion-based inpainting after rendering the point-cloud at one pixel per point.\n\nThe results on efficiency and robustness wrt point-cloud density both compare only with PFGS, not with the other baselines. These results should be added to give a more complete picture of relative performance.\n\nThe work ignores the fundamental issue that point-cloud rendering is ill-posed – unless we know a true physical scale for each point, there is not enough information to know whether a given set of nearby, coplanar points in fact represent a continuous, solid surface rather than some kind of holed, porous structure. Both gaussian splats and NeRFs are more explicitly 'solid' in this sense since they define densities over non-infinitesimal volumes. This point should be discussed in the introduction, which currently is presented as if there is one true", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "b4ItHo2fzV", "reviewer": "Reviewer_Z48r", "summary": "This paper proposes a model, called DiffPBR, that generates photo-realistic, view-consistent point-cloud renderings. DiffPBR is a two-stage method that combines adaptive, noise-consistent splatting with a diffusion model to render point clouds from calibrated camera view parameters. First, the point cloud is projected into the image domain using point-based splatting with a globally truncated scatter-distance threshold. This step introduces noise and artificial rasterization artifacts, serving as an initial rendering proposal. Then, a DDPM flow-based backbone is trained end to end to produce denoised, photo-realistic renderings for each camera view.\n\nTo show that DiffPBR is an end-to-end solution for rendering single-modal point-cloud input, the authors conduct experiments on the ScanNet, DTU, and THuman 2.0 datasets to demonstrate (i) improved rendering quality and (ii) manageable rendering speed (**2**–10 FPS, depending on Quality mode or Efficient mode, which the authors refer to as DiffPBR-Q and DiffPBR-E, respectively).", "questions": "* Can you define the CNSplat function (L246–L248) as a mathematical formula?\n* In L49, does FPGS refer to PFGS?\n* Can you explain why a graphics-based rendering technique can fail to provide photorealistic renderings on the given datasets? Have you considered rendering the ScanNet or DTU data using Mitsuba rather than PyTorch3D?\n* In L714, should $\\mathcal{F}$ be $\\mathcal{F}_{\\theta}$?\n* Apart from Figure 6 (noise-covariance patterns across views) and average rendering performance, have you considered other schemes to quantify view consistency of the rendering output? I don’t have an immediate answer in mind, but I would like to hear more about how to safeguard “consistent multi-view synthesis” (L93).", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper offers an insightful focus on decoupling the point cloud information from the rendering pipeline’s noise scheduling. Point-based splatting is much lighter than PFGS’s per-point CNN scale predictor and is explicitly tuned to produce good inputs for the diffusion refiner rather than the final image.\n\nThe idea of adopting a diffusion model that diffuses from something other than pure Gaussian noise recalls [Cold Diffusion](https://proceedings.neurips.cc/paper_files/paper/2023/file/80fe51a7d8d0c73ff7439c2a2554ed53-Paper-Conference.pdf), which might indicate its efficacy for this particular problem.\n\nThe experiments are self-contained, and the ablations are extensive.", "weaknesses": "My biggest concern is the training setup given what is already known about the input. The assumption that the point cloud and calibrated camera views are available is both an advantage and a restriction. Is DiffPBR aware of noise arising from point-cloud registration, and if it is trained per scene, why is it not biased, and how can it transfer or generalize across scenes? I understand Figure 5 tries to address transferability by directly migrating models without fine-tuning, but I am unsure why such a scheme would work. More experiments may be needed to convince the reader that geometry-aware noise patterns are interchangeable.\n\nIn my view, recent works like [Vanilla Gaussian Splatting](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/), [FSGS](https://zehaozhu.github.io/FSGS/), and [RPBG](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/02379.pdf) should be included for comparison even though they are NVS methods that rely on (sparse) multi-view input. Comparing to their results would provide supportive evidence for why DiffPBR can outperform and better utilize point-cloud information. My suggestion is to test bilaterally: give one NVS method a full prior of point-cloud information, and give DiffPBR the sparse-view COLMAP registration results in the large-scale scenarios used in those NVS papers. These comparisons would make the paper more compelling.\n\nSome minor concerns, if resolved, would also improve the completeness of the paper, and one could consider raising the score if most concerns are addressed or justified.\n\n* No code is provided for reproducibility.\n* L204–206, Eqn. (1): using the median of all average scales seems a very bold guess. What happens if a different percentile (the median is the 50th percentile) is used, and why is the mean not an appropriate statistic?\n* In L283 A1 L11, why is the residual ground truth given as a linear interpolant of $I_{\\epsilon}^v$ and $I_{r}^v$? Can you explain $\\gamma_t$, as it seems undefined.\n* Why do DiffPBR-E and DiffPBR-Q require the same training time on average per scene? I think their training pipelines can be interchangeable, and DiffPBR-E may be more generalizable since it uses a relative image-consistency loss rather than comparing with ground truth. Have you considered cross-checking whether DiffPBR-Q training plus one-step inference would work?\n* I notice that in Table 2, PFGS is reported to have 3.6 FPS while DiffPBR-Q only gets 2 FPS inference speed. Please clarify the claim in L24 of the abstract.\n* Although the ablation is extensive, the choices of $\\lambda_{cov}=0.01$ and $\\lambda_{cmp}=0.1$ in L864 are not discussed. Do they affect performance?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5Rpqgykjzj", "reviewer": "Reviewer_6AMT", "summary": "This paper proposes DiffPBR, a point-based rendering framework that integrates a diffusion model to enhance the quality and view consistency of images rendered from colored point clouds. The method consists of three main components: 1) An adaptive \"CoNo-Splatting\" technique that rasterizes both color and a structured 3D noise map from the point cloud; 2) A residual diffusion process that learns to predict the difference between the coarse rendering and the ground truth, conditioned on the rendered noise and a mask; 3) A learnable global scale parameter optimized via a balance of coverage and compactness losses. The authors demonstrate state-of-the-art results on several datasets (ScanNet, DTU, THuman2.0) in terms of PSNR/SSIM/LPIPS, while also reporting significant improvements in training efficiency and inference speed compared to the baseline PFGS.", "questions": "1. Why was a direct quantitative and qualitative comparison with 3D Gaussian Splatting (3DGS) omitted? Given that 3DGS represents the current peak performance for this problem (even if scene-specific), how does DiffPBR's rendering quality on a per-scene basis compare?\n2. The joint optimization of the splatting stage (via $L_ens$) and the diffusion model (via $L_dm$) is non-standard. Can you provide an ablation study training the diffusion model with a fixed β (e.g., from a pre-trained stage or a heuristic) to demonstrate that the joint training is truly beneficial and not detrimental to convergence?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The overall framework is well-motivated and addresses the core artifacts of point-based rendering (holes, aliasing, view inconsistency) in a unified, learnable manner. The integration of a diffusion model for refinement is a logical and powerful choice.", "weaknesses": "1. Limited Conceptual Novelty: The core components—\"residual diffusion\" and \"3D-consistent noise\"—are applications of existing ideas rather than fundamental innovations. Residual learning is standard in image restoration, and 3D-consistent generation is a well-established research direction. The paper does not sufficiently delineate its conceptual advance beyond the specific application to point cloud rendering.\n2. Inadequate Comparison to True SOTA: The most significant omission is a direct comparison with 3D Gaussian Splatting (3DGS) and its variants, which are the current undisputed state-of-the-art in terms of rendering quality and speed for scene-specific novel view synthesis. Bypassing this comparison undermines the claim of superior performance.\n3. Unconvincing and Unfair Efficiency Claims: The dramatic reduction in training time compared to PFGS is likely attributable to the replacement of PFGS's heavy CNN-based point refinement network with a much simpler rendering front-end. This is an architectural choice, not necessarily a pure algorithmic efficiency gain, making the comparison somewhat misleading.\n4. Methodological Ambiguities: The end-to-end joint training of the splatting parameters (β) and the diffusion model creates a non-stationary training target for the diffusion network, the stability and necessity of which are not validated through ablations. The use of the term \"adversarial balance\" for a simple weighted sum of losses is inaccurate and misleading.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "vQEjgyuxli", "reviewer": "Reviewer_Vaw3", "summary": "This paper proposes DiffPBR, a diffusion-based framework for point-based rendering to produce photorealistic and view-consistent images directly from point clouds. The method introduces two core components: (a) Adaptive CoNo-Splatting, a differentiable point rasterizer that adaptively adjusts point scales using a learnable global regularizer; (b) Spatial-Aware Residual Diffusion (RDDM) – a diffusion model conditioned on geometry-aware, view-consistent noise maps and trained to predict residuals. The two model variants, -Q and -E, have shown large improvements over the baseline method of PFGS.", "questions": "- What does the PyTorch3D in the experiments particularly mean? Do the authors mean directly rasterizing points by PyTorch3D? If so, it shouldn't be a baseline method since PyTorch3D is just an implementation.\n- What if Adaptive CoNo-Splatting is applied to other methods? I wonder its performance as a plug-and-play module.\n- Also it seems that the authors have misused the tex commands of \\citep{} and \\citet{}. Please correct these citation formats in the modified manuscript accordingly.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The idea of applying noise mechanism in 3D space (rather than 2D) is very interesting to me, and the implementation does achieve satisfactory results on common benchmarks. The residual diffusion formulation is elegant and well-motivated, reducing computational cost while preserving fidelity.\n- The pipeline is well-illustrated and the pseudo-code & the appendix demonstrate commendable clarity and reproducibility for readers.\n- The proposed CoNo-Splatting has the potential to be developed as a common plug-in for most point-based rendering methods.", "weaknesses": "- The two claimed contributions—Adaptive CoNo-Splatting and Spatial-Aware Residual Diffusion—are somehow independent, with limited conceptual or algorithmic coupling. Each component could function as a standalone contribution, making the overall framework appear as a loose combination rather than a tightly integrated method.\n- The evaluation of performance is not sufficient, in terms of both benchmarks and baselines. The three datasets are either bounded (ScanNet) or captured under well-controlled lab environment. More challenging cases such as Tanks and Temples are recommended to see the real potential of these methods. For baselines, I think the authors should at least include 3DGS as it's one of the most known method for point-based rendering.\n- The rendering fps is not suitable for real-time applications, though it's not considered as a drawback of this work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)