Method

Densely Constrained Depth Estimator for Monocular 3D Object Detection [DCD]
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Submitted on 5 Mar. 2022 08:54 by
Cong Pan (UCAS)

Running time:0.03 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
ECCV2022: https://arxiv.org/abs/2207.10047
Parameters:
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Latex Bibtex:
@inproceedings{li2022densely,
title={Densely Constrained Depth Estimator for
Monocular 3D Object Detection},
author={Li, Yingyan and Chen, Yuntao and He,
Jiawei and Zhang, Zhaoxiang},
booktitle={European Conference on Computer
Vision},
pages={718--734},
year={2022},
organization={Springer}
}

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) 96.44 % 90.93 % 83.36 %
Car (Orientation) 96.31 % 90.66 % 83.01 %
Car (3D Detection) 23.81 % 15.90 % 13.21 %
Car (Bird's Eye View) 32.55 % 21.50 % 18.25 %
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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