  We present Co-SLAM, a neural RGB-D SLAM system based on a hybrid
representation, that performs robust camera tracking and high-fidelity surface
reconstruction in real time. Co-SLAM represents the scene as a multi-resolution
hash-grid to exploit its high convergence speed and ability to represent
high-frequency local features. In addition, Co-SLAM incorporates one-blob
encoding, to encourage surface coherence and completion in unobserved areas.
This joint parametric-coordinate encoding enables real-time and robust
performance by bringing the best of both worlds: fast convergence and surface
hole filling. Moreover, our ray sampling strategy allows Co-SLAM to perform
global bundle adjustment over all keyframes instead of requiring keyframe
selection to maintain a small number of active keyframes as competing neural
SLAM approaches do. Experimental results show that Co-SLAM runs at 10-17Hz and
achieves state-of-the-art scene reconstruction results, and competitive
tracking performance in various datasets and benchmarks (ScanNet, TUM, Replica,
Synthetic RGBD). Project page: https://hengyiwang.github.io/projects/CoSLAM
