title: Steepest Descent Density Control for Compact 3D Gaussian Splatting
summary: 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for
  real-time, high-resolution novel view synthesis. By representing scenes as a
  mixture of Gaussian primitives, 3DGS leverages GPU rasterization pipelines for
  efficient rendering and reconstruction. To optimize scene coverage and capture
  fine details, 3DGS employs a densification algorithm to generate additional
  points. However, this process often leads to redundant point clouds, resulting
  in excessive memory usage, slower performance, and substantial storage demands
  - posing significant challenges for deployment on resource-constrained devices.
  To address this limitation, we propose a theoretical framework that demystifies
  and improves density control in 3DGS. Our analysis reveals that splitting is
  crucial for escaping saddle points. Through an optimization-theoretic approach,
  we establish the necessary conditions for densification, determine the minimal
  number of offspring Gaussians, identify the optimal parameter update direction,
  and provide an analytical solution for normalizing off-spring opacity. Building
  on these insights, we introduce SteepGS, incorporating steepest density
  control, a principled strategy that minimizes loss while maintaining a compact
  point cloud. SteepGS achieves a ~50% reduction in Gaussian points without
  compromising rendering quality, significantly enhancing both efficiency and
  scalability.
paper_authors._id: 6821d45b18636d528174eb5c
paper_authors.name: Peihao Wang
paper_authors._id: 6821d45b18636d528174eb5d
paper_authors.name: Yuehao Wang
paper_authors._id: 6821d45b18636d528174eb5e
paper_authors.name: Dilin Wang
paper_authors._id: 6821d45b18636d528174eb5f
paper_authors.name: Sreyas Mohan
paper_authors._id: 6821d45b18636d528174eb60
paper_authors.name: Zhiwen Fan
paper_authors._id: 6821d45b18636d528174eb61
paper_authors.name: Lemeng Wu
paper_authors._id: 6821d45b18636d528174eb62
paper_authors.name: Ruisi Cai
paper_authors._id: 6821d45b18636d528174eb63
paper_authors.name: Yu-Ying Yeh
paper_authors._id: 6821d45b18636d528174eb64
paper_authors.name: Zhangyang Wang
paper_authors._id: 6821d45b18636d528174eb65
paper_authors.name: Qiang Liu
paper_authors._id: 6821d45b18636d528174eb66
paper_authors.name: Rakesh Ranjan
paper_title: Steepest Descent Density Control for Compact 3D Gaussian Splatting
paper_summary: 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for
  real-time, high-resolution novel view synthesis. By representing scenes as a
  mixture of Gaussian primitives, 3DGS leverages GPU rasterization pipelines for
  efficient rendering and reconstruction. To optimize scene coverage and capture
  fine details, 3DGS employs a densification algorithm to generate additional
  points. However, this process often leads to redundant point clouds, resulting
  in excessive memory usage, slower performance, and substantial storage demands
  - posing significant challenges for deployment on resource-constrained devices.
  To address this limitation, we propose a theoretical framework that demystifies
  and improves density control in 3DGS. Our analysis reveals that splitting is
  crucial for escaping saddle points. Through an optimization-theoretic approach,
  we establish the necessary conditions for densification, determine the minimal
  number of offspring Gaussians, identify the optimal parameter update direction,
  and provide an analytical solution for normalizing off-spring opacity. Building
  on these insights, we introduce SteepGS, incorporating steepest density
  control, a principled strategy that minimizes loss while maintaining a compact
  point cloud. SteepGS achieves a ~50% reduction in Gaussian points without
  compromising rendering quality, significantly enhancing both efficiency and
  scalability.
paper_ai_summary: A theoretical framework and optimization strategy to improve density control in 3D Gaussian Splatting reduce memory usage and enhance efficiency without sacrificing rendering quality.
paper_ai_keywords: Gaussian Splatting GPU rasterization densification algorithm Gaussian primitives density control splitting optimization-theoretic approach SteepGS steepest density control rendering quality efficiency scalability