地面点群を除去しても物体検出は可能か？障害物認識型点群圧縮
Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection
遠隔物体検出のために、物体認識に不要な地面点群を軽量アルゴリズムで除去し、検出性能を維持しつつ圧縮率を向上させる手法を提案。
著者: Pengxi Zeng, Alberto Presta, Jonah Reinis, Dinesh Bharadia, Hang Qiu, Pamela Cosman
分類: cs.CV, cs.RO
原文アブストラクト
Efficient point cloud (PC) compression is crucial for streaming applications, such as augmented reality and cooperative perception. Classic PC compression techniques encode all the points in a frame. Tailoring compression towards perception tasks at the receiver side, we ask the question, "Can we remove the ground points during transmission without sacrificing the detection performance?" Our study reveals a strong dependency on the ground from state-of-the-art (SOTA) 3D object detection models, especially on those points below and around the object. In this work, we propose a lightweight obstacle-aware Pillar-based Ground Removal (PGR) algorithm. PGR filters out ground points that do not provide context to object recognition, significantly improving compression ratio without sacrificing the receiver side perception performance. Not using heavy object detection or semantic segmentation models, PGR is light-weight, highly parallelizable, and effective. Our evaluations on KITTI and Waymo Open Dataset show that SOTA detection models work equally well with PGR removing 20-30% of the points, with a speeding of 86 FPS.
