NeuV-SLAM: Fast Neural Multiresolution Voxel Optimization for RGBD Dense
  SLAM
Wenzhi Guo Bing Wang Lijun Chen
We introduce NeuV-SLAM, a novel dense simultaneous localization and mapping
pipeline based on neural multiresolution voxels, characterized by ultra-fast
convergence and incremental expansion capabilities. This pipeline utilizes RGBD
images as input to construct multiresolution neural voxels, achieving rapid
convergence while maintaining robust incremental scene reconstruction and
camera tracking. Central to our methodology is to propose a novel implicit
representation, termed VDF that combines the implementation of neural signed
distance field (SDF) voxels with an SDF activation strategy. This approach
entails the direct optimization of color features and SDF values anchored
within the voxels, substantially enhancing the rate of scene convergence. To
ensure the acquisition of clear edge delineation, SDF activation is designed,
which maintains exemplary scene representation fidelity even under constraints
of voxel resolution. Furthermore, in pursuit of advancing rapid incremental
expansion with low computational overhead, we developed hashMV, a novel
hash-based multiresolution voxel management structure. This architecture is
complemented by a strategically designed voxel generation technique that
synergizes with a two-dimensional scene prior. Our empirical evaluations,
conducted on the Replica and ScanNet Datasets, substantiate NeuV-SLAM's
exceptional efficacy in terms of convergence speed, tracking accuracy, scene
reconstruction, and rendering quality.