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arxiv: 2404.06109 · v1 · pith:SQ6EUUFEnew · submitted 2024-04-09 · 💻 cs.CV

Revising Densification in Gaussian Splatting

classification 💻 cs.CV
keywords densificationcontroldensitygaussianlimitationsmethodscenesplatting
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In this paper, we address the limitations of Adaptive Density Control (ADC) in 3D Gaussian Splatting (3DGS), a scene representation method achieving high-quality, photorealistic results for novel view synthesis. ADC has been introduced for automatic 3D point primitive management, controlling densification and pruning, however, with certain limitations in the densification logic. Our main contribution is a more principled, pixel-error driven formulation for density control in 3DGS, leveraging an auxiliary, per-pixel error function as the criterion for densification. We further introduce a mechanism to control the total number of primitives generated per scene and correct a bias in the current opacity handling strategy of ADC during cloning operations. Our approach leads to consistent quality improvements across a variety of benchmark scenes, without sacrificing the method's efficiency.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Rectifying Mask via Entropy for Distractor-Free 3DGS in Ambiguous Scenarios

    cs.CV 2026-06 unverdicted novelty 6.0

    RefineSplat applies entropy-aware adaptive masking and density control to 3DGS to remove color- or semantically ambiguous distractors, validated on a new 18-scene Ambiguous wild dataset with claimed SOTA results.

  2. Two-View Accumulation as the Primary Training Lever for Hybrid-Capture Gaussian Splatting: A Variance-Decomposition View of When Gradient Surgery Helps

    cs.CV 2026-04 unverdicted novelty 5.0

    Two-view accumulation per optimizer step is the dominant training lever for hybrid-capture 3DGS, explained by a variance-decomposition framework showing within-regime gradient variance dominates over between-regime variance.