Recent advancements in RGB-only dense Simultaneous Localization and Mapping
(SLAM) have predominantly utilized grid-based neural implicit encodings and/or
struggle to efficiently realize global map and pose consistency. To this end,
we propose an efficient RGB-only dense SLAM system using a flexible neural
point cloud scene representation that adapts to keyframe poses and depth
updates, without needing costly backpropagation. Another critical challenge of
RGB-only SLAM is the lack of geometric priors. To alleviate this issue, with
the aid of a monocular depth estimator, we introduce a novel DSPO layer for
bundle adjustment which optimizes the pose and depth of keyframes along with
the scale of the monocular depth. Finally, our system benefits from loop
closure and online global bundle adjustment and performs either better or
competitive to existing dense neural RGB SLAM methods in tracking, mapping and
rendering accuracy on the Replica, TUM-RGBD and ScanNet datasets. The source
code will be made available.

提出了一种基于灵活的神经网络点云场景表示的高效 RGB-only 稠密 SLAM 系统，通过适应关键帧姿态和深度更新，优化了位姿和深度，并在回环检测、全局束调整等方面取得了较好或有竞争力的跟踪、建图和渲染准确性。