发表机构
Toronto Metropolitan University; University of Windsor; University College London（多伦多都会大学; 温莎大学; 伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Pow3R-SLAM，一种利用深度作为先验的实时RGB-D SLAM系统，通过双视图重建提升跟踪与建图精度，在多个基准上优于现有方法。
AI 中文摘要
我们提出了Pow3R-SLAM，一个使用Pow3R进行跟踪和建图的实时RGB-D同时定位与建图（SLAM）系统。受MASt3R-SLAM（一项近期使用双视图3D重建先验的单目SLAM工作）的启发，我们扩展了该工作，将深度作为网络预测的先验，而非作为待融合的几何信息。在传统RGB-D SLAM系统难以处理深度图像稀疏性的场景中，Pow3R利用可用的深度信息获得条件更好的点图，同时从双视图光度、深度和内参数据中推断空白区域的深度。在TUM、7-Scenes和Replica的24个序列上，按照MASt3R-SLAM的协议进行评估，Pow3R-SLAM的墙钟时间运行速度快1.6倍，平均轨迹误差降低15%，未缩放误差降低3.1倍，并生成更密集的地图，Chamfer距离降低30%。我们还引入了一种混合变体，其运行速度比MASt3R-SLAM快2.1倍，达到每秒25.3帧（FPS），同时保持改进的跟踪和建图精度。与RGB-D模式下的ORB-SLAM3相比，Pow3R-SLAM在TUM、7-Scenes和ETH3D-SLAM上更准确，并完成了所有TUM序列。虽然Pow3R-SLAM在一小部分自相似场景上可能表现不佳，但其整体性能表明，将深度作为双视图3D重建SLAM的先验是有益的。项目网页可在此https URL获取，代码将在论文被接收后开源。
英文摘要
We present Pow3R-SLAM, a real-time RGB-D simultaneous localization and mapping (SLAM) system that uses Pow3R for tracking and mapping. Inspired by MASt3R-SLAM, a recent work on monocular SLAM using two-view 3D reconstruction priors, we extend the work to incorporate depth as a prior on the network's prediction, rather than as geometry to fuse. Where traditional RGB-D SLAM systems struggle with sparsity in the depth images, Pow3R utilizes the available depth to give a better-conditioned pointmap, while inferring the depths in empty regions from the two-view photometric, depth, and intrinsic data. Evaluated against MASt3R-SLAM following its protocol on 24 sequences from TUM, 7-Scenes, and Replica, Pow3R-SLAM runs 1.6x faster in wall time, has 15% lower mean trajectory error, a 3.1x lower unscaled error, and produces denser maps, with a 30% lower Chamfer distance. We also introduce a hybrid variant that runs 2.1x faster than MASt3R-SLAM at 25.3 frames per second (FPS), while maintaining improved tracking and mapping accuracy. Against ORB-SLAM3 in RGB-D mode, Pow3R-SLAM is more accurate on TUM, 7-Scenes, and ETH3D-SLAM, and completes every TUM sequence. While Pow3R-SLAM can struggle on a small set of self-similar scenes, its overall performance shows that adding depth as a prior for two-view 3D reconstruction SLAM can be beneficial. A project webpage is available at: https://ChrisKolios.github.io/Pow3R-SLAM , and code will be made open-source upon acceptance.
Comments9 pages, 4 figures, 4 tables. Project page: https://chriskolios.github.io/Pow3R-SLAM/
