Method

CLOCs [CLOCs_PVCas]
https://github.com/pangsu0613/CLOCs

Submitted on 1 Aug. 2020 03:23 by
Su Pang (MIchigan State University)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@inproceedings{pang2020CLOCs,
title={CLOCs: Camera-LiDAR Object Candidates
Fusion for 3D Object Detection },
author={Pang, Su and Morris, Daniel and Radha,
Hayder},
booktitle={2020 IEEE/RSJ International
Conference on Intelligent Robots and Systems
(IROS)},
year={2020},
organization={IEEE}
}

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) 96.76 % 95.96 % 91.08 %
Car (Orientation) 96.74 % 95.79 % 90.81 %
Car (3D Detection) 88.94 % 80.67 % 77.15 %
Car (Bird's Eye View) 93.05 % 89.80 % 86.57 %
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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