Method

Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation [st] [Disp R-CNN (velo)]
https://github.com/zju3dv/disprcnn

Submitted on 5 Jul. 2020 09:16 by
Yiming Xie (Zhejiang University)

Running time:0.42 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
with LiDAR points as optimization target for pseudo-
GT generation.
Parameters:
as detailed in the paper
Latex Bibtex:
@inproceedings{sun2020disprcnn,
title={Disp R-CNN: Stereo 3D Object Detection via
Shape Prior Guided Instance Disparity Estimation},
author={Sun, Jiaming and Chen, Linghao and Xie,
Yiming and
Zhang, Siyu and Jiang, Qinhong and Zhou, Xiaowei
and Bao, Hujun},
booktitle={CVPR},
year={2020}
}

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Car (Detection) 93.20 % 82.47 % 68.09 %
Car (Orientation) 92.86 % 81.67 % 67.22 %
Car (3D Detection) 59.61 % 39.36 % 32.01 %
Car (Bird's Eye View) 74.12 % 52.37 % 43.79 %
Pedestrian (Detection) 68.84 % 51.31 % 46.80 %
Pedestrian (Orientation) 60.06 % 43.99 % 39.79 %
Pedestrian (3D Detection) 30.98 % 21.98 % 18.68 %
Pedestrian (Bird's Eye View) 35.39 % 24.95 % 21.30 %
Cyclist (Detection) 58.27 % 42.25 % 36.90 %
Cyclist (Orientation) 50.66 % 35.77 % 30.96 %
Cyclist (3D Detection) 39.72 % 23.75 % 20.47 %
Cyclist (Bird's Eye View) 43.41 % 26.46 % 22.46 %
This table as LaTeX


2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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