Method

Deep Stereo Geometry Network [st] [DSGN]
https://github.com/chenyilun95/DSGN

Submitted on 22 Nov. 2019 16:40 by
Yilun Chen (CUHK)

Running time:0.67 s
Environment:NVIDIA Tesla V100

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@inproceedings{Chen2020dsgn,
title={DSGN: Deep Stereo Geometry Network for 3D
Object Detection},
author={Yilun Chen and
Shu Liu and
Xiaoyong Shen and
Jiaya Jia},
journal={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) 95.53 % 86.43 % 78.75 %
Car (Orientation) 95.42 % 86.03 % 78.27 %
Car (3D Detection) 73.50 % 52.18 % 45.14 %
Car (Bird's Eye View) 82.90 % 65.05 % 56.60 %
Pedestrian (Detection) 49.28 % 39.93 % 38.13 %
Pedestrian (Orientation) 31.21 % 24.32 % 23.09 %
Pedestrian (3D Detection) 20.53 % 15.55 % 14.15 %
Pedestrian (Bird's Eye View) 26.61 % 20.75 % 18.86 %
Cyclist (Detection) 49.10 % 35.15 % 31.41 %
Cyclist (Orientation) 29.76 % 20.28 % 19.13 %
Cyclist (3D Detection) 27.76 % 18.17 % 16.21 %
Cyclist (Bird's Eye View) 31.23 % 21.04 % 18.93 %
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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