SHIFT：面向TSDF的表面感知高速集成方法
SHIFT: Surface-aware High-speed Integration For TSDFs
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- Indian Institute of Technology Jodhpur（焦特布尔印度理工学院）
- BotLab Dynamics
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
SHIFT通过压缩平坦区域为超射线并冻结体素梯度，降低TSDF集成成本1.42-4.07倍，减少ESDF内存28%，实现高效实时建图。
中文摘要 AI 辅助
实时三维建图是自主机器人导航的基础，而欧几里得符号距离场（ESDF）作为在线运动规划的标准表示。尽管近期非投影距离场的进展产生了高度精确的地图，但其计算开销仍然是一个严重的瓶颈。传统集成器每帧冗余地对数百万个深度像素进行重新融合，即使在相应体素已收敛之后仍如此，在由大型平面表面主导的环境中浪费了大量计算资源。本文提出了SHIFT（面向TSDF的表面感知高速集成方法），一种旨在降低每帧更新成本的高效建图框架。通过直接从3D深度几何中利用结构冗余，SHIFT将平坦局部区域压缩为加权超射线，并冻结平坦体素的梯度。紧凑的ESDF体素布局进一步减少了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将TSDF成本降低了1.42至4.07倍，同时将网格误差保持在毫米级，并将ESDF层内存减少高达28%。
英文摘要
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%
