Boosting Performance on 3D Object Detection with a Plug-in Discrimination Module | |
Yi Yang1,2![]() ![]() | |
2024-05 | |
会议名称 | International Conference on Machine Vision and Information Technology (CMVIT) |
会议日期 | 2024.02.23 |
会议地点 | Singapore |
摘要 | Around-view multi-camera 3D object detection in BEV (Bird's-Eye-View) space has been a research focus over the past few years. As a typical supervised training task, many researchers promote this area with different task-specific key designs, such as exploiting temporal information and correspondence of perspective image plane and BEV space. Most of these works follow the DETR detection framework, yet the nature of learnable queries in DETR, the encodings of objects' center and bounding box information, have not been discussed in previous studies. In this paper, we take advantage of this prior and further extend it to 3D detection tasks. In 3D object detection, the ground-truth bounding boxes are hardly overlapping. Therefore, the queries should be more diverse under this hypothesis. To achieve this goal, we propose a Plug-in Discrimination Module (PDM) to discriminate learnable queries from all the other queries with a discrimination loss to ensure the diversity of queries. The PDM is a simple train-time-only module. It contains a query projection head to project all the object queries into a common latent space. In the latent space, the discrimination loss is conducted on all the queries. Experimental results show that this design can directly improve the 3D detector's performance without modifying the detector's architecture and adding extra inference costs. The NDS improvement on the nuScenes dataset is up to a maximum of 1.62% in the 8th training epoch and remains an average 0.64% improvement in the following epochs, compared with the baseline model. |
收录类别 | EI |
WOS记录号 | IOP:JPCS_2759_1_012008 |
七大方向——子方向分类 | 三维视觉 |
国重实验室规划方向分类 | 环境多维感知 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57362 |
专题 | 模式识别实验室 |
通讯作者 | Zhang Zhang |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Science(UCAS) 2.Institute of Automation, Chinese Academy of Science. Beijing, China |
推荐引用方式 GB/T 7714 | Yi Yang,Zhang Zhang. Boosting Performance on 3D Object Detection with a Plug-in Discrimination Module[C],2024. |
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