  Neural RGBD SLAM techniques have shown promise in dense Simultaneous
Localization And Mapping (SLAM), yet face challenges such as error accumulation
during camera tracking resulting in distorted maps. In response, we introduce
Loopy-SLAM that globally optimizes poses and the dense 3D model. We use
frame-to-model tracking using a data-driven point-based submap generation
method and trigger loop closures online by performing global place recognition.
Robust pose graph optimization is used to rigidly align the local submaps. As
our representation is point based, map corrections can be performed efficiently
without the need to store the entire history of input frames used for mapping
as typically required by methods employing a grid based mapping structure.
Evaluation on the synthetic Replica and real-world TUM-RGBD and ScanNet
datasets demonstrate competitive or superior performance in tracking, mapping,
and rendering accuracy when compared to existing dense neural RGBD SLAM
methods. Project page: notchla.github.io/Loopy-SLAM.
