提出统一框架，让任意图像集都能快速做三维重建与实时定位。
BLASt3R: Bundle Adjustment of Any Image Set with Multi-View Matching and Monocular Priors
- 用快速多视角匹配+单目先验初始化优化，提升鲁棒性。
- 在在线和离线场景中均超越传统方法，速度与精度更优。
- 适合需快速重建或实时定位的系统，如机器人导航。
近期混合式结构光恢复（SfM）系统结合前馈3D重建的鲁棒性与传统束调整（BA）像素匹配的高精度，通常表现最佳，但其可扩展性和可用性受限于视图间密集对应关系估计成本过高，尤其在视觉同步定位与地图构建（VSLAM）等实时应用中时间压力大。本文提出一种正则化束调整框架，利用快速多视角匹配器和单目先验进行初始化与正则化。与现有系统不同，该统一方法在相同优化框架下无缝支持在线VSLAM与无序图像集离线重建，并共享所有任务的通用超参数。跨两个领域的大规模实验表明，本方法在性能与速度权衡上优于传统、前馈及混合基线。尤其在VSLAM中，未校准的方法超越了所有先前校准方案。
原文摘要 · Abstract (English)
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.
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