解决多车协同3D检测中的空间错位问题，提升感知精度。
CoGoal3D: Collaborative 3D Object Detection with 3D-Aware Fusion and Refinement
- 分两阶段融合3D特征，缓解不同车辆高度姿态带来的错位。
- 3D点重建辅助任务显著提升检测结果，最高提升10.86%。
- 适合自动驾驶多车协同感知场景，尤其关注3D精度的团队。
车联网协同感知通过整合多车环境特征，克服单车系统的局限。然而，现有主流方法多聚焦于2D BEV检测，面对3D检测任务时效果不佳，因忽略了协作车辆间因高度和姿态差异导致的3D空间错位。本文提出新型协同3D目标检测框架CoGoal3D，采用两阶段渐进式提取与优化3D特征。第一阶段设计多尺度3D感知全局融合模块，缓解3D空间错位；第二阶段通过3D点重建辅助任务对候选框进行精炼。进一步提出一种高效的多车协同数据增强策略，在丰富训练数据的同时最小化信息损失。在多个公开真实世界数据集上的实验表明，CoGoal3D达到新基准性能，在DAIR-V2X、V2V4Real和V2X-Real数据集上3D [email protected]分别提升10.86%、10.34%和10.18%。代码已开源。
原文摘要 · Abstract (English)
V2X collaborative object detection features overcoming the limitations of single-vehicle systems by aggregating environmental features from multiple collaborative agents. However, existing mainstream V2X perception methods mainly focus on 2D BEV object detection. When 3D detection task is concerned, inferior results are obtained because they ignore the 3D spatial misalignment caused by differing height and attitude among the collaborators. In this paper, we propose a novel collaborative 3D object detection framework called CoGoal3D, which extracts and refines the 3D feature gradually in a two-stage pipeline. In the first stage, a multiscale 3D-aware global fusion module is designed to mitigate the 3D spatial misalignment. The resulting proposals are then refined in the second stage with an auxiliary task of 3D point reconstruction. An effective multi-agent collaborative data augmentation strategy is further proposed to enrich the training data while minimizing information loss. Extensive experiments on public real-world datasets demonstrate that our CoGoal3D achieves new state-of-the-art performance, with 3D [email protected] improvements of 10.86%, 10.34%, and 10.18% on the DAIR-V2X, V2V4Real, and V2X-Real datasets, respectively. Code is available at https://github.com/Megalo-f/CoGoal3D.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。