提出高效3D地图构建方法，显著降低计算开销且保持高精度
SHIFT: Surface-aware High-speed Integration For TSDFs
- 通过压缩平面区域为加权超射线，减少重复更新
- 每帧计算成本降低1.42至4.07倍，网格误差在毫米级
- 适合实时机器人导航与资源受限设备使用
实时3D建图是自主机器人导航的基础，欧几里得有符号距离场（ESDF）是在线运动规划的标准表示。尽管非投影距离场已实现高精度地图，其计算开销仍是严重瓶颈。传统融合器每帧冗余地重融合数百万个深度像素，即使对应体素早已收敛，在以大平面为主的环境中浪费大量计算资源。本文提出SHIFT（Surface-aware High-speed Integration For TSDFs），一种高效的映射框架，旨在降低每帧更新成本。通过直接利用3D深度几何中的结构冗余，SHIFT将平坦局部区域压缩为加权超射线，并冻结平面体素梯度。紧凑的ESDF体素布局进一步降低剩余波前的内存占用。在多种RGB-D和LiDAR序列上的广泛评估表明，SHIFT使TSDF成本降低1.42至4.07倍，同时保持网格误差在毫米级，且ESDF层内存减少高达28%。
原文摘要 · Abstract (English)
Real-time 3D mapping is fundamental for autonomous robotic navigation, with Euclidean Signed Distance Fields (ESDFs) serving as the standard representation for online motion planning. While recent advancements in non- projective distance fields yield highly accurate maps, their computational overhead remains a severe bottleneck. Conventional integrators redundantly re-fuse millions of depth pixels every frame, even long after the corresponding voxels have converged, wasting significant computational resources in environments dominated by large planar surfaces. In this paper, we present SHIFT (Surface-aware High-speed Integration For TSDFs), an efficient mapping framework designed to reduce this per-frame update cost. By exploiting structural redundancy directly from 3D depth geometry, SHIFT compresses flat local regions into weighted super-rays and freezes flat-voxel gradients. A compact ESDF voxel layout further reduces the memory footprint of the remaining wavefront. Extensive evaluations across various RGB-D and LiDAR sequences show that SHIFT cuts TSDF cost by 1.42 to 4.07 times, while holding mesh error within millimeters, and reduces ESDF-layer memory by up to 28%
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