arxiv: 2605.13093 · v1 · submitted 2026-05-13 · 💻 cs.CV

Recognition: unknown

RoSplat: Robust Feed-Forward Pixel-wise Gaussian Splatting for Varying Input Views and High-Resolution Rendering

Hoang Chuong Nguyen, Jose M. Alvarez, Miaomiao Liu, Renjie Wu

Authors on Pith no claims yet

Pith reviewed 2026-05-14 19:58 UTC · model grok-4.3

classification 💻 cs.CV

keywords Gaussian splattingnovel view synthesisfeed-forward renderingalpha normalization3D regularizationhigh-resolution renderinggeneralizable reconstruction

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The pith

Alpha normalization and a 3D regularizer make pixel-wise Gaussian splatting produce consistent brightness and fewer holes regardless of input view count or output resolution.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper targets two practical failures in feed-forward 3D Gaussian splatting for novel-view synthesis: renderings become too bright when the number of input views changes, and holes appear at high resolution because Gaussian scales are poorly estimated. The authors trace the brightness problem to the changing number of overlapping Gaussians per pixel and correct it with a simple alpha normalization step. They further add a 3D sampling regularizer that improves scale accuracy and removes the holes. Experiments show these two changes lift the performance of existing baseline models on standard benchmarks under both varying-view and high-resolution conditions.

Core claim

Existing pixel-wise feed-forward methods suffer from over-bright renderings when the number of input views varies during inference, as well as insufficient supervision for accurate Gaussian scale estimation, which leads to hole artifacts, particularly in high-resolution renderings. To address these issues, we identify that the over-brightness is caused by the varying number of overlapping Gaussians and propose a simple alpha normalization strategy to maintain brightness consistency across different number of input views. In addition, we introduce an auxiliary 3D sampling-based regularizer to improve Gaussian scale estimation, thereby mitigating hole artifacts in high-resolution rendering.

What carries the argument

Alpha normalization that scales output brightness according to the number of overlapping Gaussians, combined with an auxiliary 3D sampling-based regularizer that supplies additional supervision on Gaussian scale.

If this is right

Renderings keep the same brightness level when the number of input views changes at test time.
High-resolution outputs contain fewer holes because Gaussian scales are estimated more accurately.
Baseline feed-forward models improve on benchmark datasets under both varying-view and high-resolution test conditions.
The entire pipeline remains feed-forward and efficient while gaining robustness to view count.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same normalization idea could be applied to other point-based or splatting representations to achieve view-count invariance.
Capture pipelines could drop the requirement for a fixed number of cameras without needing extra post-processing.
Integration with dynamic or video-based scenes might extend the consistency gains beyond static novel-view synthesis.

Load-bearing premise

Over-brightness arises only from changes in the number of overlapping Gaussians and the normalization plus regularizer will not create new artifacts on unseen scenes or data distributions.

What would settle it

Render the same target viewpoint once with three input views and once with five input views, then check whether the two outputs have identical pixel brightness values that also match a ground-truth photograph.

Figures

Figures reproduced from arXiv: 2605.13093 by Hoang Chuong Nguyen, Jose M. Alvarez, Miaomiao Liu, Renjie Wu.

**Figure 1.** Figure 1: Left: DepthSplat [23] exhibits hole artifacts due to its predicted small-scale Gaussians. Our method mitigate the hole issues and produce more complete view. Right: Results overview: As the number of input views and rendering resolution increase, our method consistently achieves better image quality than DepthSplat. regions using either graph neural networks [28] or pixel-alignment structures [21]. While e… view at source ↗

**Figure 2.** Figure 2: Overall pipeline. We introduce two components into the existing pixel-wise Gaussian prediction framework. First, alpha normalization is integrated into the rendering process to improve robustness to varying numbers of input views. Second, a 3D sampling-based regularizer L3D promotes accurate Gaussian scale estimation, mitigating hole artifacts under high-resolution rendering [PITH_FULL_IMAGE:figures/full… view at source ↗

**Figure 3.** Figure 3: Alpha normalization adjusts each Gaussian’s contribution based on its overlap count to avoid overbright rendering when increasing the number of input views [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 5.** Figure 5: Qualitative results on RealEstate10K. Integrating alpha normalization helps to mitigate the overbrightness issues encountered by [6, 23] when increasing the number of input views [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: High-resolution rendering on DL3DV dataset. Our method significantly alleviates the holes issues exhibited by DepthSplat [23] [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: Ablation study on DL3DV. Alpha normalization improves the robustness to varying input views, while 3D sampling-based regularizer improves high-resolution rendering. improve the image quality, with the performance gain increase proportionally to the image resolution. For the PSNR metric, on average, our methods outperforms the baseline models by 0.52 dB, 1.26 dB and 1.71 dB while rendering images at 2×, 4× … view at source ↗

read the original abstract

Generalizable 3D Gaussian Splatting has recently emerged as an efficient approach for novel-view synthesis, enabling feed-forward synthesis from only a few input views. However, existing pixel-wise feed-forward methods suffer from over-bright renderings when the number of input views varies during inference, as well as insufficient supervision for accurate Gaussian scale estimation, which leads to hole artifacts, particularly in high-resolution renderings. To address these issues, we identify that the over-brightness is caused by the varying number of overlapping Gaussians and propose a simple alpha normalization strategy to maintain brightness consistency across different number of input views. In addition, we introduce an auxiliary 3D sampling-based regularizer to improve Gaussian scale estimation, thereby mitigating hole artifacts in high-resolution rendering. Experiments on benchmark datasets demonstrate that our method significantly improves baseline models under varying input-view and high-resolution rendering settings.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

RoSplat adds alpha normalization for brightness stability and a 3D sampling regularizer for scale accuracy in feed-forward Gaussian splatting, but the abstract gives no numbers or ablations to judge the fixes.

read the letter

The main takeaway is that this paper targets two concrete problems in pixel-wise feed-forward Gaussian splatting: over-bright renderings when input view count changes at inference, and hole artifacts at high resolution from poor scale estimates. They trace the brightness issue to varying numbers of overlapping Gaussians and propose a simple alpha normalization to keep output consistent. They also add an auxiliary 3D sampling regularizer to give better supervision on Gaussian scales. These are incremental but directly useful tweaks for anyone running these models with variable inputs or needing clean high-res output. The abstract frames the fixes clearly as responses to observed failures in prior work, which is a strength. It also claims benchmark gains under the exact settings that matter for applications like AR or robotics. That said, the abstract contains no quantitative results, no ablation tables, and no error breakdowns, so the size of the improvement and whether the fixes interact badly remain unclear. The stress-test point about view-dependent radiance is worth watching—if the predicted Gaussians carry spherical harmonics or similar view-dependent color, normalizing only alpha could shift radiance incorrectly across views. If the full paper does not test cross-view consistency or include the interaction analysis, that would be a real gap. This work is aimed at researchers already building or extending generalizable Gaussian splatting pipelines who hit these artifacts in practice. A reader in that niche would pick up usable implementation ideas. I would send it to peer review because the problems are real and the proposed solutions are specific enough to merit referee input, even if the validation needs more detail.

Referee Report

2 major / 1 minor

Summary. The paper proposes RoSplat, a feed-forward pixel-wise 3D Gaussian Splatting method for novel view synthesis from sparse inputs. It diagnoses over-bright renderings as arising from varying counts of overlapping Gaussians across different numbers of input views and introduces a simple alpha normalization fix for brightness consistency. It further adds an auxiliary 3D sampling-based regularizer to improve Gaussian scale estimation and thereby reduce hole artifacts during high-resolution rendering. Experiments are claimed to show significant improvements over baselines on standard benchmarks under varying-view and high-res settings.

Significance. If the fixes prove robust, the work would address two practical failure modes that currently limit deployment of generalizable feed-forward Gaussian Splatting pipelines. The explicit identification of the overlap-count cause for brightness drift and the addition of a 3D regularizer for scale are concrete contributions that could be adopted by follow-on methods.

major comments (2)

[Abstract] Abstract: the claim that over-brightness is caused solely by the varying number of overlapping Gaussians is presented without supporting derivation, ablation, or per-pixel contribution analysis; the proposed alpha normalization is described only at a high level, leaving open whether it preserves view-consistent radiance when Gaussians carry view-dependent features or spherical harmonics.
[Abstract] The interaction between the alpha normalization and the 3D sampling regularizer is not analyzed; it is possible that correcting brightness via normalization exposes or creates new scale-related artifacts once the regularizer is applied, yet no joint ablation or sensitivity study is referenced.

minor comments (1)

The abstract would be strengthened by including at least one key quantitative metric (e.g., PSNR or LPIPS delta) under the varying-view and high-resolution protocols.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the opportunity to clarify our contributions. We provide point-by-point responses below.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that over-brightness is caused solely by the varying number of overlapping Gaussians is presented without supporting derivation, ablation, or per-pixel contribution analysis; the proposed alpha normalization is described only at a high level, leaving open whether it preserves view-consistent radiance when Gaussians carry view-dependent features or spherical harmonics.

Authors: The abstract provides a concise summary of the motivation and method. The detailed derivation of how varying Gaussian overlaps lead to brightness inconsistency is presented in Section 3.1 of the manuscript, with the mathematical formulation of the alpha accumulation. An ablation study isolating the effect of alpha normalization is included in Section 4.2, along with per-pixel visualizations in Figure 4. For view-dependent features, the normalization is applied post-color computation but pre-blending, ensuring that spherical harmonics and view-dependent effects remain unchanged. We will update the abstract to briefly mention the supporting sections for clarity. revision: partial
Referee: [Abstract] The interaction between the alpha normalization and the 3D sampling regularizer is not analyzed; it is possible that correcting brightness via normalization exposes or creates new scale-related artifacts once the regularizer is applied, yet no joint ablation or sensitivity study is referenced.

Authors: We agree that a more explicit analysis of the interaction would strengthen the paper. In the current manuscript, Table 5 presents results with both components enabled, showing additive improvements without degradation. However, we acknowledge the lack of a dedicated joint sensitivity study. We will add a new paragraph in Section 4.4 discussing the interaction, including additional experiments on varying the regularization weight alongside normalization to confirm no new artifacts are introduced. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims are empirical proposals without reduction to inputs

full rationale

The paper's core claims rest on an empirical identification of over-brightness from varying Gaussian overlaps, followed by direct proposal of alpha normalization and a 3D sampling regularizer. No equations, self-citations, or derivations are provided in the text that reduce these to fitted parameters, self-definitions, or prior author results by construction. The strategies are framed as responses to observed issues in baselines, preserving independent content.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Builds on the established 3D Gaussian Splatting representation and feed-forward prediction paradigm from prior literature; no new free parameters, axioms, or invented entities are specified in the abstract beyond standard domain assumptions of the base framework.

axioms (1)

domain assumption 3D Gaussian Splatting provides an efficient differentiable rendering representation for scenes from input views
The method extends pixel-wise feed-forward Gaussian splatting, inheriting its core scene representation assumptions.

pith-pipeline@v0.9.0 · 5461 in / 1253 out tokens · 66720 ms · 2026-05-14T19:58:12.242402+00:00 · methodology

discussion (0)

Reference graph

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