Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection

TL;DR

This paper introduces a lightweight obstacle-aware ground removal method for point cloud compression that maintains detection accuracy while significantly reducing data size and increasing processing speed.

Contribution

The authors propose PGR, a simple, parallelizable ground removal algorithm that improves compression efficiency without degrading detection performance.

Findings

Removing 20-30% of ground points does not affect detection accuracy.

PGR increases processing speed to 86 FPS.

The method is effective on KITTI and Waymo datasets.

Abstract

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.