2D GauSS-MI: Efficient Active Scene Reconstruction with Balanced Visual and Geometric Quality

2026-09-18

AI Summary

本文提出2D GauSS-MI方法，通过结合视觉和几何质量的评估来高效选择视角，以实现在有限计算资源下的高质量场景重建。

Abstract

Active reconstruction requires efficient active view selection to achieve high-quality reconstruction within limited onboard computational resources. Existing methods face challenges in adequately balancing visual and geometric quality with the computational efficiency required for real-time operation. In this work, we present an active reconstruction framework based on 2D Gaussian Splatting (2DGS). We develop an efficient online 2DGS mapping pipeline for incremental RGB-D observations and introduce a probabilistic reliability model that characterizes the view-dependent reconstruction quality of individual 2D Gaussian splats. Building on this model, we formulate 2D Gaussian Splatting Shannon Mutual Information (2D GauSS-MI), a mutual-information-based metric that exploits the explicit surface orientation of 2DGS to evaluate the expected information gain of candidate views. The proposed metric enables active view selection to account for both visual and geometric reconstruction quality. We evaluate the proposed system against three state-of-the-art baselines on eight Replica scenes. Experimental results demonstrate that our method achieves a favorable balance between visual and geometric reconstruction quality with substantially lower computational cost and competitive model storage.

Problem

• active reconstruction
• computational efficiency
• visual quality
• geometric quality
• real-time operation

Innovation

• 2D Gaussian Splatting
• Probabilistic Reliability Model
• Shannon Mutual Information