NICE-SLAM is a dense visual SLAM system that combines the advantages of neural implicit representations and hierarchical grid-based scene representation. However, the hierarchical grid features are densely stored, leading to memory explosion problems when adapting the framework to large scenes. In our project, we present sparse NICE-SLAM, a sparse SLAM system incorporating the idea of Voxel Hashing into NICE-SLAM framework. Instead of initializing feature grids in the whole space, voxel features near the surface are adaptively added and optimized. Experiments demonstrated that compared to NICE-SLAM algorithm, our approach takes much less memory and achieves comparable reconstruction quality on the same datasets. Our implementation is available at https://github.com/zhangganlin/NICE-SLAM-with-Adaptive-Feature-Grids.
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