ActivePolicy: Active Gaussian Reconstruction and Optimization Strategy Based on Global-Local Information Gain

摘要

Active 3D Gaussian reconstruction achieves superior completeness and rendering quality by intelligently selecting viewpoints. However, existing methods suffer from two critical limitations: information gain metrics that prioritize geometric coverage while ignoring rendering quality, and overfitting to sparse view configurations that degrades novel view synthesis. We introduce ActivePolicy, a novel framework addressing both challenges through principled NBV selection and regularization. We propose GL-Graph, a graph-theoretic strategy that unifies geometric consistency, rendering quality, and observation redundancy into a single stability criterion. To counteract overfitting, we introduce 4D-Reg, which identifies floaters through manifold discrepancies among three depth types (R-Depth, alpha-Depth, C-Depth) and suppresses them via adaptive dropout. Extensive experiments demonstrate state-of-the-art reconstruction completeness and rendering fidelity on standard benchmarks.