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

Source: [DetVPCC: RoI-based Point Cloud Sequence Compression for 3D Object Detection](<https://www.sota2.com/research/paper/68fa76e81e209131cea6fdc6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:05:09.252Z

Authors: Mingxuan Yan, Ruijie Zhang, Xuedou Xiao, Wei Wang

Published: 2025-02-07T10:16:15.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2502.04804>)

[PDF](<https://arxiv.org/pdf/2502.04804.pdf>)