Method

Geometry-based Distance Decomposition for Monocular 3D Object Detection [MonoRCNN]
https://github.com/Rock-100/MonoDet

Submitted on 27 Feb. 2021 03:41 by
Jack (Imperial College London)

Running time:0.07 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@inproceedings{MonoRCNN,
author = {Xuepeng Shi and Qi Ye and Xiaozhi
Chen and Chuangrong Chen and Zhixiang Chen and Tae-
Kyun Kim},
title = {Geometry-based Distance
Decomposition for Monocular 3D Object Detection},
booktitle = {ICCV},
year = {2021}
}

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) 91.98 % 86.78 % 66.97 %
Car (Orientation) 91.90 % 86.48 % 66.71 %
Car (3D Detection) 18.36 % 12.65 % 10.03 %
Car (Bird's Eye View) 25.48 % 18.11 % 14.10 %
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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