Late Breaking Results: A Fast Nearest Neighbor Search Acceleration for 3D Point Cloud

This paper presents FastNN, a novel accelerator architecture for efficient K-Nearest Neighbors (KNN) search in point clouds. FastNN leverages a locality-sensitive E2LSH partitioning method and a precomparator module to significantly reduce the candidate search space and minimize the number of Euclidean distance calculations. Compared to octree-based partitioning methods, our approach reduces candidate points by 58.57% to 86.17% and achieves a $10.04 \times$ acceleration in processing throughput relative to the BitNN comparator subsystem. The proposed design effectively enhances search throughput, resource utilization, and precision, highlighting its potential for accelerating KNN search on FPGA platforms.