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arXiv · 2610.27108

Counting Contributions, Not Views: Occlusion-Aware Densification for 3D Gaussian Splatting

Marcel Rogge·René Schuster·Didier Stricker
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Abstract

3D Gaussian Splatting grows a dense scene from a sparse initialization through adaptive density control (ADC), driven by per-Gaussian statistics gathered during rendering. These statistics pass a frustum-based visibility filter blind to occlusion: fully occluded Gaussians still count as observed, diluting the averaged gradient signal and suppressing densification precisely where geometry is occluded. We correct this with an occlusion-aware filter that restricts densification statistics to views in which a Gaussian actually contributes, and replace the criterion's raw view-count normalizer with an average-contribution one. Integrated into a StopThePop-based framework, the two together improve every image-quality metric over the occlusion-blind baseline on occlusion-heavy scenes (up to 6% lower LPIPS), at less than half the Gaussians a view-count normalizer requires. Under the longer schedules these large scenes require, the count stays bounded where most criteria diverge, and on smaller benchmarks our method stays competitive at a fraction of the competitors' count.

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Marcel Rogge, René Schuster, Didier Stricker. 2026-10-19. Counting Contributions, Not Views: Occlusion-Aware Densification for 3D Gaussian Splatting. https://arxiv.org/abs/2610.27108

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