Papers › Vehicle Detection from 3D Lidar Using Fully Convolutional Network
Vehicle Detection from 3D Lidar Using Fully Convolutional Network
Bo Li, Tianlei Zhang, Tian Xia
Convolutional network techniques have recently achieved great success in vision based detection tasks. This paper introduces the recent development of our research on transplanting the fully convolutional network technique to the detection tasks on 3D range scan data. Specifically, the scenario is set as the vehicle detection task from the range data of Velodyne 64E lidar. We proposes to present the data in a 2D point map and use a single 2D end-to-end fully convolutional network to predict the objectness confidence and the bounding boxes simultaneously. By carefully design the bounding box encoding, it is able to predict full 3D bounding boxes even using a 2D convolutional network. Experiments on the KITTI dataset shows the state-of-the-art performance of the proposed method.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Detection | KITTI Cars Easy | VeloFCN | AP | 60.34 | #5 of 5 | Archive leaderboard | report |
| Object Detection | KITTI Cars Hard | VeloFCN | AP | 42.74 | #5 of 5 | Archive leaderboard | report |
| Object Detection | KITTI Cars Moderate | VeloFCN | AP | 47.51 | #4 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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