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

Recent hybrid Structure-from-Motion (SfM) systems com-bine the robustness of feed-forward 3D reconstruction with the accuracyof traditional bundle adjustment (BA) with pixel matching. They areusually the best performing methods however their scalability and us-ability remains limited since estimating dense correspondences betweenviews is prohibitively costly, especially considering time constraints in-herent 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 supportsboth online VSLAM and offline reconstruction from unordered image col-lections within the same optimization framework and sharing commonhyperparameters for all tasks. Extensive experiments across both do-mains demonstrate improved performance and speed tradeoffs over tra-ditional, feed-forward, and hybrid baselines. Notably for VSLAM, ouruncalibrated method outperforms all previous calibrated approaches.