针对点云检测的压缩难题，提出基于感兴趣区域的高效压缩方法。
DetVPCC: RoI-based Point Cloud Sequence Compression for 3D Object Detection
- 通过非均匀质量分配实现重点区域优先压缩
- 在nuScenes数据集上显著提升3D检测准确率
- 适合自动驾驶中点云序列的实时检测应用
尽管符合MPEG标准的基于视频的点云压缩（VPCC）在人眼感知下具有高压缩效率，但在支持3D物体检测时，比特率节省与检测精度之间存在较差的权衡。这源于VPCC无法对点云中不同区域的重要性进行区分。为此，我们提出DetVPCC，一种将感兴趣区域（RoI）编码与VPCC结合的新方法，在保持3D物体检测精度的同时实现高效的点云序列压缩。具体而言，我们扩展了VPCC以支持基于RoI的压缩，通过分配空间非均匀的质量等级；同时引入轻量级RoI检测器识别可能包含物体的关键区域。在nuScenes数据集上的实验表明，该方法显著提升了检测精度。代码和演示视频见附录材料。
原文摘要 · Abstract (English)
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.
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