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

• URL: http://arxiv.org/abs/2605.13093v1
• Date: Wed, 13 May 2026 07:05:15 GMT
• Title: RoSplat: Robust Feed-Forward Pixel-wise Gaussian Splatting for Varying Input Views and High-Resolution Rendering
• Abstract summary: Generalizable 3D Gaussian Splatting has recently emerged as an efficient approach for novel-view synthesis.<n>Existing pixel-wise feed-forward methods suffer from over-bright renderings when the number of input views varies during inference.<n>We propose a simple alpha normalization strategy to maintain brightness consistency across different number of input views.
• Score: 13.57744967574079
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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