KAM-Net: Keypoint-Aware and Keypoint-Matching Network for Vehicle Detection from 2-D Point Cloud

Tianpei Zou, Guang Chen, Zhijun Li, Wei He, Sanqing Qu, Shangding Gu, Alois Knoll

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Two-dimesional (2-D) LiDAR is an efficient alternative sensor for vehicle detection, which is one of the most critical tasks in autonomous driving. Compared to the fully developed 3-D LiDAR vehicle detection, 2-D LiDAR vehicle detection has much room to improve. Most existing state-of-the-art works represent 2-D point clouds as pseudo-images and then perform detection with traditional object detectors on 2-D images. However, they ignore the sparse representation and geometric information of vehicles in the 2-D cloud points. To address these issues, in this article, we present a novel keypoint-aware and keypoint-matching network termed as KAM-Net, which focuses on better detecting the vehicles by explicitly capturing and extracting the sparse information of L-shape in 2-D LiDAR point clouds. The whole framework consists of two stages - namely, keypoint-aware stage and keypoint-matching stage. The keypoint-aware stage utilizes the heatmap and edge extraction module to simultaneously predict the position of L-shaped keypoints and inflection offset of L-shaped endpoints. The keypoint-matching stage is followed to group the keypoints and produce the oriented bounding boxes with axis by utilizing the endpoint-matching and L-shaped-matching methods. Further, we conduct extensive experiments on a recently released public dataset to evaluate the effectiveness of our approach. The results show that our KAM-Net achieves a new state-of-the-art performance. The source code is available at https://github.com/ispc-lab/KAM-Net.

Original languageEnglish
Pages (from-to)207-217
Number of pages11
JournalIEEE Transactions on Artificial Intelligence
Issue number2
StatePublished - 1 Apr 2022


  • Artificial intelligence algorithmic design and analysis
  • artificial intelligence in transportation
  • deep learning
  • supervised learning


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