用方向位图加速点云融合，实现在CPU上高效构建高分辨率3D地图。
DB-TSDF: Directional Bitmask-based Truncated Signed Distance Fields for Efficient Volumetric Mapping
- 基于方向位图的增量融合，每帧处理时间恒定。
- 在不同体素分辨率下保持稳定速度，支持高精度重建。
- 纯CPU实现，适合资源受限设备部署。
本文提出一种基于截断有符号距离场（TSDF）的高效、仅需CPU的体积化建图框架。系统通过方向位图融合方案，将原始激光雷达点云增量式融合进体素网格，生成密集且一致的TSDF表示，适用于实时3D重建。该方法的关键优势在于，每帧点云的处理时间不随体素网格分辨率变化，从而在保持运行效率的同时实现高分辨率建图。与多数依赖GPU加速的最新TSDF/ESDF方法不同，本方法完全在CPU上运行，仍能取得具有竞争力的速度表现。在真实世界公开数据集上的实验表明，生成的地图精度达到当前主流建图技术水平。
原文摘要 · Abstract (English)
This paper presents a high-efficiency, CPU-only volumetric mapping framework based on a Truncated Signed Distance Field (TSDF). The system incrementally fuses raw LiDAR point-cloud data into a voxel grid using a directional bitmask-based integration scheme, producing dense and consistent TSDF representations suitable for real-time 3D reconstruction. A key feature of the approach is that the processing time per point-cloud remains constant, regardless of the voxel grid resolution, enabling high resolution mapping without sacrificing runtime performance. In contrast to most recent TSDF/ESDF methods that rely on GPU acceleration, our method operates entirely on CPU, achieving competitive results in speed. Experiments on real-world open datasets demonstrate that the generated maps attain accuracy on par with contemporary mapping techniques.
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