Method

MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships [MonoPair]


Submitted on 15 Nov. 2019 09:49 by
Lei Tai (HKUST; Alibaba, AI Lab)

Running time:0.06 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
We propose to improve the monocular 3D object
detection by considering the relationship of paired
samples.
Parameters:
LR 1.25e-4, WC 1e-5
Latex Bibtex:
@InProceedings{chen2020cvpr,
author = {Yongjian Chen and Lei Tai and Kai Sun and
Mingyang Li},
title = {MonoPair: Monocular 3D Object Detection
Using Pairwise Spatial Relationships},
booktitle = {The IEEE Conference on Computer Vision
and Pattern Recognition (CVPR)},
month = {June},
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) 96.61 % 93.55 % 83.55 %
Car (Orientation) 91.65 % 86.11 % 76.45 %
Car (3D Detection) 13.04 % 9.99 % 8.65 %
Car (Bird's Eye View) 19.28 % 14.83 % 12.89 %
Pedestrian (Detection) 78.81 % 61.57 % 56.51 %
Pedestrian (Orientation) 55.26 % 42.38 % 38.53 %
Pedestrian (3D Detection) 10.02 % 6.68 % 5.53 %
Pedestrian (Bird's Eye View) 10.99 % 7.04 % 6.29 %
Cyclist (Detection) 74.77 % 56.37 % 48.37 %
Cyclist (Orientation) 53.36 % 39.47 % 33.95 %
Cyclist (3D Detection) 3.79 % 2.12 % 1.83 %
Cyclist (Bird's Eye View) 4.76 % 2.87 % 2.42 %
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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