← 研踪 PaperTrailCVF · CVPR 2026:cvf:55b4ff0bdb804ed4e8923dc04bde99c0 · 记录 #1
Dynamic Visual SLAM using a General 3D Prior
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 02:36
摘要
Reliable incremental estimation of camera poses and 3D reconstruction is key to enable various applications including robotics, interactive visualization, and augmented reality. However, online SLAM is particularly challenging in dynamic natural environments, where scene dynamics can severely deteriorate camera pose estimation accuracy. In this work, we propose a novel monocular visual SLAM system that can robustly estimate camera poses in dynamic scenes. We leverage the complementary strengths of geometric patch-based online bundle adjustment and recent feed-forward reconstruction models. Specifically, we propose a feed-forward reconstruction model to precisely filter out dynamic regions, while also utilizing its depth prediction to enhance the robustness of the patch-based visual SLAM. By aligning depth prediction with estimated patches from bundle adjustment, we handle the inherent scale ambiguities of the batch-wise application of the feed-forward reconstruction model. Extensive experiments on multiple tasks show the superior performance of our proposed method compared to state-of-the-art approaches.
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记录 #1 · 2026 · 最新观测版本
Dynamic Visual SLAM using a General 3D Prior
Reliable incremental estimation of camera poses and 3D reconstruction is key to enable various applications including robotics, interactive visualization, and augmented reality. However, online SLAM is particularly challenging in dynamic natural environments, where scene dynamics can severely deteriorate camera pose estimation accuracy. In this work, we propose a novel monocular visual SLAM system that can robustly estimate camera poses in dynamic scenes. We leverage the complementary strengths of geometric patch-based online bundle adjustment and recent feed-forward reconstruction models. Specifically, we propose a feed-forward reconstruction model to precisely filter out dynamic regions, while also utilizing its depth prediction to enhance the robustness of the patch-based visual SLAM. By aligning depth prediction with estimated patches from bundle adjustment, we handle the inherent scale ambiguities of the batch-wise application of the feed-forward reconstruction model. Extensive experiments on multiple tasks show the superior performance of our proposed method compared to state-of-the-art approaches.
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