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.

翻译：NICE-SLAM 是一种密集视觉SLAM系统，它结合了神经隐式表示与层级网格场景表示的优势。然而，层级网格特征采用密集存储方式，当将框架适配到大场景时会导致内存爆炸问题。在我们的项目中，我们提出了稀疏NICE-SLAM，这是一种将体素哈希思想融入NICE-SLAM框架的稀疏SLAM系统。该方案不再在整个空间中初始化特征网格，而是自适应地添加和优化表面附近的体素特征。实验表明，与NICE-SLAM算法相比，我们的方法内存占用显著降低，且在相同数据集上实现了相当的重建质量。我们的实现代码已开源在 https://github.com/zhangganlin/NICE-SLAM-with-Adaptive-Feature-Grids。