BLASt3R: Bundle Adjustment of Any Image Set with Multi-View Matching and Monocular Priors

2026-09-04

AI Summary

本文提出了一种结合多视图匹配和单目先验的正则化束调整框架，解决了在线VSLAM与离线无序图像重建中的性能和效率问题。

Abstract

Recent hybrid Structure-from-Motion (SfM) systems combine the robustness of feed-forward 3D reconstruction with the accuracy of traditional bundle adjustment (BA) with pixel matching. They are usually the best performing methods however their scalability and usability remains limited since estimating dense correspondences between views is prohibitively costly, especially considering time constraints inherent to online applications like Visual SLAM (VSLAM). In this paper, we introduce a regularized BA framework that leverages a fast multi-view matcher and monocular priors for initialization and regularization. In contrast to existing systems, our unified approach seamlessly supports both online VSLAM and offline reconstruction from unordered image collections within the same optimization framework and sharing common hyperparameters for all tasks. Extensive experiments across both domains demonstrate improved performance and speed tradeoffs over traditional, feed-forward, and hybrid baselines. Notably for VSLAM, our uncalibrated method outperforms all previous calibrated approaches.

Problem

• Structure-from-Motion
• bundle adjustment
• Visual SLAM
• multi-view matching
• monocular priors

Innovation

• regularized BA
• multi-view matching
• monocular priors
• unified approach
• shared hyperparameters