DetVPCC: RoI-based Point Cloud Sequence Compression for 3D Object Detection

TL;DR

DetVPCC introduces a region-of-interest based point cloud compression method that enhances 3D object detection accuracy while maintaining high compression efficiency, addressing limitations of traditional VPCC.

Contribution

We propose a novel RoI-based point cloud compression method integrated with VPCC, improving detection accuracy without sacrificing compression efficiency.

Findings

Significantly improves 3D detection accuracy on nuScenes dataset

Supports non-uniform quality levels in point cloud compression

Maintains high compression efficiency comparable to standard VPCC

Abstract

While MPEG-standardized video-based point cloud compression (VPCC) achieves high compression efficiency for human perception, it struggles with a poor trade-off between bitrate savings and detection accuracy when supporting 3D object detectors. This limitation stems from VPCC's inability to prioritize regions of different importance within point clouds. To address this issue, we propose DetVPCC, a novel method integrating region-of-interest (RoI) encoding with VPCC for efficient point cloud sequence compression while preserving the 3D object detection accuracy. Specifically, we augment VPCC to support RoI-based compression by assigning spatially non-uniform quality levels. Then, we introduce a lightweight RoI detector to identify crucial regions that potentially contain objects. Experiments on the nuScenes dataset demonstrate that our approach significantly improves the detection accuracy. The code and demo video are available in supplementary materials.