Coverage Optimization for Camera View Selection

2026-04-06

AI Summary

This work proposes an efficient active view selection method to enhance 3D scene reconstruction quality by prioritizing camera viewpoints that maximize information gain. The approach introduces the COVER metric, which optimizes geometric coverage while minimizing a computationally tractable approximation of Fisher information gain, thereby favoring observations of under-explored regions. Unlike existing methods, COVER eliminates the need for costly transmittance estimation, offering an interpretable and lightweight view planning strategy that maintains robustness and computational efficiency. Experimental results demonstrate that the proposed method significantly outperforms current active view selection techniques across multiple real-world datasets and NeRF baselines integrated within Nerfstudio.

Abstract

What makes a good viewpoint? The quality of the data used to learn 3D reconstructions is crucial for enabling efficient and accurate scene modeling. We study the active view selection problem and develop a principled analysis that yields a simple and interpretable criterion for selecting informative camera poses. Our key insight is that informative views can be obtained by minimizing a tractable approximation of the Fisher Information Gain, which reduces to favoring viewpoints that cover geometry that has been insufficiently observed by past cameras. This leads to a lightweight coverage-based view selection metric that avoids expensive transmittance estimation and is robust to noise and training dynamics. We call this metric COVER (Camera Optimization for View Exploration and Reconstruction). We integrate our method into the Nerfstudio framework and evaluate it on real datasets within fixed and embodied data acquisition scenarios. Across multiple datasets and radiance-field baselines, our method consistently improves reconstruction quality compared to state-of-the-art active view selection methods. Additional visualizations and our Nerfstudio package can be found at https://chengine.github.io/nbv_gym/.

Problem

• view selection
• coverage optimization
• 3D reconstruction
• camera pose
• active perception

Innovation

• view selection
• coverage optimization
• Fisher Information Gain
• 3D reconstruction
• NeRF