Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM

• URL: http://arxiv.org/abs/2304.14377v1
• Date: Thu, 27 Apr 2023 17:46:45 GMT
• Title: Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM
• Authors: Hengyi Wang, Jingwen Wang, Lourdes Agapito
• Abstract summary: Co-SLAM is an RGB-D SLAM system based on a hybrid representation. It performs robust camera tracking and high-fidelity surface reconstruction in real time.
• Score: 14.56883275492083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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