发表机构
NAVER LABS Europe（NAVER欧洲实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
BLASt3R提出结合多视图匹配与单目先验的正则化BA框架，支持在线VSLAM和离线无序图像重建，在性能速度权衡上优于基线，其未标定VSLAM方法性能超此前所有标定方法。
AI 中文摘要
近期的混合运动恢复结构（SfM）系统结合了前馈三维重建的鲁棒性与带像素匹配的传统光束平差法（BA）的精度，通常是性能最优的方法，但它们的可扩展性和可用性仍受限制，因为估计视图间的密集对应关系成本过高，尤其是考虑到视觉SLAM（VSLAM）等在线应用固有的时间约束。本文提出一种正则化BA框架，利用快速多视图匹配器和单目先验进行初始化与正则化。与现有系统不同，该统一方法在同一优化框架内无缝支持在线VSLAM和无序图像集的离线重建，所有任务共享超参数。在两个领域的大量实验表明，该方法相比传统、前馈和混合基线在性能与速度的权衡上有所提升。值得注意的是，对于VSLAM，该未标定方法优于所有先前的标定方法。
英文摘要
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.
CommentsECCV'26
