CoGoal3D: Collaborative 3D Object Detection with 3D-Aware Fusion and Refinement

V2X collaborative object detection features overcoming thelimitations of single-vehicle systems by aggregating environmental fea-tures from multiple collaborative agents. However, existing mainstreamV2X perception methods mainly focus on 2D BEV object detection.When 3D detection task is concerned, inferior results are obtained be-cause they ignore the 3D spatial misalignment caused by differing heightand attitude among the collaborators. In this paper, we propose a novelcollaborative 3D object detection framework called CoGoal3D, whichextracts and refines the 3D feature gradually in a two-stage pipeline. Inthe first stage, a multiscale 3D-aware global fusion module is designedto mitigate the 3D spatial misalignment. The resulting proposals arethen refined in the second stage with an auxiliary task of 3D point re-construction. An effective multi-agent collaborative data augmentationstrategy is further proposed to enrich the training data while minimizinginformation loss. Extensive experiments on public real-world datasetsdemonstrate that our CoGoal3D achieves new state-of-the-art perfor-mance, with 3D AP@0.7 improvements of 10.86%, 10.34%, and 10.18%on the DAIR-V2X, V2V4Real, and V2X-Real datasets, respectively.