Papers › PIXOR: Real-time 3D Object Detection from Point Clouds
PIXOR: Real-time 3D Object Detection from Point Clouds
Bin Yang, Wenjie Luo, Raquel Urtasun
We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. Computation speed is critical as detection is a necessary component for safety. Existing approaches are, however, expensive in computation due to high dimensionality of point clouds. We utilize the 3D data more efficiently by representing the scene from the Bird's Eye View (BEV), and propose PIXOR, a proposal-free, single-stage detector that outputs oriented 3D object estimates decoded from pixel-wise neural network predictions. The input representation, network architecture, and model optimization are especially designed to balance high accuracy and real-time efficiency. We validate PIXOR on two datasets: the KITTI BEV object detection benchmark, and a large-scale 3D vehicle detection benchmark. In both datasets we show that the proposed detector surpasses other state-of-the-art methods notably in terms of Average Precision (AP), while still runs at >28 FPS.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Birds Eye View Object Detection | KITTI Cars Easy | PIXOR | AP | 81.7% | #9 of 9 | Archive leaderboard | report |
| Birds Eye View Object Detection | KITTI Cars Hard | PIXOR | AP | 72.95 | #8 of 8 | Archive leaderboard | report |
| Birds Eye View Object Detection | KITTI Cars Moderate | PIXOR | AP | 77.05% | #9 of 9 | Archive leaderboard | report |
| Birds Eye View Object Detection | KITTI Cars Moderate val | PIXOR | AP | 80.75 | #2 of 3 | 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.
Methods
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