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.

翻译：我们提出NeuV-SLAM，一种基于神经多分辨率体素的新型稠密同步定位与建图方案，其核心特征在于超快收敛与增量扩展能力。该方案以RGBD图像为输入构建多分辨率神经体素，在保持稳健的增量式场景重建与相机跟踪能力的同时实现快速收敛。我们的方法论核心是提出一种名为VDF的新型隐式表征，该表征将神经有向距离场体素的实现与SDF激活策略相结合。该方法通过对体素内锚定的颜色特征与SDF值进行直接优化，显著提升了场景收敛速度。为保证清晰边缘轮廓的获取，我们设计了SDF激活机制，即使在体素分辨率受限条件下仍能保持卓越的场景表征保真度。此外，为在低计算开销下实现快速增量扩展，我们开发了hashMV——一种新型基于哈希的多分辨率体素管理架构。该架构通过战略性设计的体素生成技术实现补充，该技术可协同利用二维场景先验。我们在Replica与ScanNet数据集上开展的实证评估，充分验证了NeuV-SLAM在收敛速度、跟踪精度、场景重建与渲染质量方面的卓越效能。