Steepest Descent Density Control for Compact 3D Gaussian Splatting
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.
6821d45b18636d528174eb5c
Peihao Wang
6821d45b18636d528174eb5d
Yuehao Wang
6821d45b18636d528174eb5e
Dilin Wang
6821d45b18636d528174eb5f
Sreyas Mohan
6821d45b18636d528174eb60
Zhiwen Fan
6821d45b18636d528174eb61
Lemeng Wu
6821d45b18636d528174eb62
Ruisi Cai
6821d45b18636d528174eb63
Yu-Ying Yeh
6821d45b18636d528174eb64
Zhangyang Wang
6821d45b18636d528174eb65
Qiang Liu
6821d45b18636d528174eb66
Rakesh Ranjan
Steepest Descent Density Control for Compact 3D Gaussian Splatting
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.
A theoretical framework and optimization strategy to improve density control in 3D Gaussian Splatting reduce memory usage and enhance efficiency without sacrificing rendering quality.
Gaussian Splatting GPU rasterization densification algorithm Gaussian primitives density control splitting optimization-theoretic approach SteepGS steepest density control rendering quality efficiency scalability