CDGS: Confidence-Aware Depth Regularization for 3D Gaussian Splatting

Code coming soon! We are currently cleaning up and documenting our implementation.

Overview

CDGS is a confidence-aware depth-based optimization strategy for 3D Gaussian Splatting (3DGS). Our method enhances the original 3DGS through two key components:

1. Depth Refinement and Alignment:

   • Utilizes Depth Anything V2 for initial depth estimation
   • Aligns monocular depth estimates with sparse SfM depth data
   • Improves geometric consistency across multiple views

2. Confidence-Aware Depth Regularization:

   • Generates confidence maps for each depth map
   • Adaptively adjusts depth loss weights during optimization
   • Enables more stable optimization process

Features

• Improved geometric detail preservation in early training stages
• More stable convergence behavior
• Comprehensive 2D-3D evaluation framework