V2X-BGN: Camera-based V2X-Collaborative 3D Object Detection with BEV Global Non-Maximum Suppression

• Published in: IEEE Intelligent Vehicles Symposium pp. 602 - 607
• Main Authors: Zhang, Caiji, Tian, Bin, Meng, Shi, Qi, Shuangying, Sun, Yang, Ai, Yunfeng, Chen, Long
• Format: Conference Proceeding
• Language: English
• Published: IEEE 02.06.2024
• Subjects: Collaboration; Laser radar; Object detection; Point cloud compression; Roads; Three-dimensional displays; Visualization
• ISSN: 2642-7214
• DOI: 10.1109/IV55156.2024.10588592

In recent years, research on Vehicle-to-Everything (V2X) cooperative perception algorithms mainly focuses on the fusion of intermediate features from LiDAR point clouds. Since the emergence of excellent single-vehicle visual perception models like BEVFormer, collaborative perception schemes based on camera and late-fusion have become feasible approaches. This paper proposes a V2X-collaborative 3D object detection structure in Bird's Eye View (BEV) space, based on global non-maximum suppression and late-fusion (V2X-BGN), and conducts experiments on the V2X-Set dataset. Focusing on complex road conditions with extreme occlusion, the paper compares the camera-based algorithm with the LiDAR-based algorithm, validating the effectiveness of pure visual solutions in the collaborative 3D object detection task. Additionally, this paper highlights the complementary potential of camera-based and LiDAR-based approaches and the importance of object-to-ego vehicle distance in the collaborative 3D object detection task.