Efficient Scene Modeling via Structure-Aware and Region-Prioritized 3D Gaussians

Area of Science:

• Computer Vision
• Computer Graphics
• 3D Scene Reconstruction

Background:

• Photometric supervision dominates current 3D Gaussian Splatting (3DGS) methods.
• This leads to inefficient spatial distribution and ignores geometric context.
• Existing methods face challenges in balancing representation compactness, convergence speed, and rendering fidelity.

Purpose of the Study:

• Introduce Mini-Splatting2, a novel framework for efficient 3D scene modeling.
• Rethink Gaussian modeling from a geometric perspective.
• Develop a geometry-regulated paradigm for 3DGS.

Main Methods:

• Implement structure-aware distribution for spatial regularity and representation sparsity.
• Employ region-prioritized optimization using geometric saliency and computational selectivity.
• Couple these mechanisms to improve training discrimination and convergence.

Main Results:

• Achieve up to 4x fewer Gaussian primitives compared to existing methods.
• Demonstrate up to 3x faster optimization times.
• Maintain state-of-the-art visual quality in 3D scene reconstruction.

Conclusions:

• Mini-Splatting2 offers a structured and efficient approach to 3D Gaussian modeling.
• The framework effectively addresses the tension between representation compactness, convergence acceleration, and rendering fidelity.
• Paves the way for geometrically-aware and optimized 3D scene reconstruction.