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SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation

Introduction

We implement SMOKE and provide the results and checkpoints on KITTI dataset.

@inproceedings{liu2020smoke,
  title={Smoke: Single-stage monocular 3d object detection via keypoint estimation},
  author={Liu, Zechen and Wu, Zizhang and T{\'o}th, Roland},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  pages={996--997},
  year={2020}
}

Results

KITTI

Backbone Lr schd Mem (GB) Inf time (fps) mAP Download
DLA34 6x 9.64 13.85 model | log

Note: mAP represents Car moderate 3D strict AP11 results.

Detailed performance on KITTI 3D detection (3D/BEV) is as follows, evaluated by AP11 metric:

Easy Moderate Hard
Car 16.92 / 22.97 13.85 / 18.32 11.90 / 15.88
Pedestrian 11.13 / 12.61 11.10 / 11.32 10.67 / 11.14
Cyclist 0.99 / 1.47 0.54 / 0.65 0.55 / 0.67