Method

MonoCD [MonoCD]
https://github.com/elvintanhust/MonoCD

Submitted on 17 Nov. 2023 02:54 by
Longfei Yan (Zhejiang University and Westlake University)

Running time:n/a s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@inproceedings{yan2024monocd,
title={MonoCD: Monocular 3D Object Detection with
Complementary Depths},
author={Yan, Longfei and Yan, Pei and Xiong,
Shengzhou and Xiang, Xuanyu and Tan, Yihua},
booktitle={CVPR},
year={2024}
}

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) 96.43 % 92.91 % 85.55 %
Car (Orientation) 96.36 % 92.65 % 85.17 %
Car (3D Detection) 25.53 % 16.59 % 14.53 %
Car (Bird's Eye View) 33.41 % 22.81 % 19.57 %
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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