用GPU加速点云配准，3倍提升实时性。
FlashReg: GPU-Accelerated 3-Clique Point Cloud Registration for Real-Time Correspondence-to-Pose Estimation
- 跳过构建密集图，直接生成稀疏二阶图
- 三元团搜索延迟降低2-3倍，内存占用减半
- 适合嵌入式设备上实时感知系统使用
基于图的点云配准通过识别几何一致的对应关系集合实现高鲁棒性，但构建二阶兼容图并枚举候选团仍存在计算和内存开销大问题。本文提出FlashReg，一种面向GPU的对应到位姿估计器，避免显式构造密集评分的二阶图。其快速一、二阶图（FFSOG）构造从二值一阶图直接生成容量受限的稀疏二阶图。数据流优化的三节点团（3-clique）搜索从紧凑的每行候选池中选取枢纽点，并通过排序的稀疏邻域交集枚举三元组。在室内与室外基准测试中，FlashReg相较于TurboReg在保持相近配准召回率的情况下，将对应到位姿的延迟降低2-3倍，且在嵌入式GPU上仅使用约其峰值张量内存的50%。该结果使FlashReg适合作为车载感知流水线中的高吞吐配准后端。
原文摘要 · Abstract (English)
Graph-based point cloud registration achieves high robustness by identifying geometrically consistent correspondence sets, but constructing second-order compatibility graphs and enumerating candidate cliques remain compute- and memory-intensive. This work presents FlashReg, a GPU-oriented correspondence-to-pose estimator that avoids materializing the dense scored second-order graph. Its Fast First- and Second-Order Graph (FFSOG) construction builds a capacity-bounded sparse second-order graph directly from the binary first-order graph. A dataflow-optimized three-node clique (3-clique) search then selects pivots from compact per-row candidate pools and enumerates triples through sorted sparse-neighborhood intersections. Across indoor and outdoor benchmarks, FlashReg reduces correspondence-to-pose latency by 2--3x relative to TurboReg at comparable registration recall, while using about 50% of its peak allocated tensor memory on an embedded GPU. These results make FlashReg suitable as a high-throughput registration backend within onboard perception pipelines.
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