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Revising Densification in Gaussian Splatting

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arxiv 2404.06109 v1 pith:SQ6EUUFE submitted 2024-04-09 cs.CV

Samuel Rota Bulò , Lorenzo Porzi , Peter Kontschieder This is my paper
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 3 Pith papers

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

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

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    RefineSplat removes ambiguous distractors from 3DGS via entropy-aware adaptive masking and density control, releasing an 18-scene Ambiguous wild dataset and reporting SOTA metrics on multiple wild benchmarks.

  2. Hybrid Mesh-Gaussian Representation for Efficient Indoor Scene Reconstruction

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A hybrid representation routes texture-rich flat indoor regions to a textured mesh and keeps Gaussians only for complex geometry, reducing Gaussian counts by 18-50% with roughly comparable rendering quality.

  3. UGOD: Uncertainty-Guided Differentiable Opacity and Soft Dropout for Enhanced Sparse-View 3DGS

    cs.CV 2025-08 conditional novelty 4.0 of 10

    A sparse-view 3D Gaussian Splatting method that learns per-Gaussian uncertainty to modulate opacity and apply differentiable soft dropout, reporting small PSNR gains over three baselines.

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