NeuV-SLAM：用于RGBD稠密SLAM的快速神经多分辨率体素优化
NeuV-SLAM: Fast Neural Multiresolution Voxel Optimization for RGBD Dense SLAM
AI总结：
NeuV-SLAM提出基于神经多分辨率体素的RGBD稠密SLAM流程，通过VDF表示和hashMV结构实现超快收敛与增量扩展，在Replica和ScanNet数据集上验证了高效性能。
摘要：
我们提出了NeuV-SLAM，一种基于神经多分辨率体素的新型稠密同时定位与建图（SLAM）流程，其特点是超快速收敛和增量扩展能力。该流程以RGBD图像作为输入，构建多分辨率神经体素，在保持稳健的增量场景重建和相机跟踪的同时实现快速收敛。我们方法的核心是提出一种名为VDF的新型隐式表示，它将神经有符号距离场（SDF）体素的实现与SDF激活策略相结合。这种方法直接优化锚定在体素内的颜色特征和SDF值，显著提高了场景收敛速度。为了确保获得清晰的边缘轮廓，我们设计了SDF激活，即使在体素分辨率受限的情况下也能保持卓越的场景表示保真度。此外，为了以低计算开销推进快速增量扩展，我们开发了hashMV，一种基于哈希的新型多分辨率体素管理结构。该架构辅以与二维场景先验协同设计的体素生成技术。我们在Replica和ScanNet数据集上进行的实证评估，证实了NeuV-SLAM在收敛速度、跟踪精度、场景重建和渲染质量方面的卓越效能。
英文摘要：
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.
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