Method

Pseudo-LiDAR [st] [Pseudo-LiDAR]
[Anonymous Submission]

Submitted on 27 Nov. 2018 16:34 by
[Anonymous Submission]

Running time:0.4 s
Environment:GPU @ 2.5 Ghz (C/C++)

Method Description:
CVPR2019: Pseudo-LiDAR from Visual Depth Estimation:
Bridging the Gap in 3D Object Detection for Autonomous
Driving
Parameters:
It is shown in our github repo.
Latex Bibtex:
@inproceedings{wangcvpr2019,
title={Pseudo-LiDAR from Visual Depth Estimation:
Bridging
the Gap in 3D Object Detection for Autonomous Driving},
author={Wang, Yan and Chao, Wei-Lun and Garg,
Divyansh
and Hariharan, Bharath and Campbell, Mark and
Weinberger, Kilian},
booktitle={CVPR},
year={2019}
}

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) 85.08 % 67.96 % 59.55 %
Car (3D Detection) 55.40 % 37.17 % 31.37 %
Car (Bird's Eye View) 66.83 % 47.20 % 40.30 %
This table as LaTeX


2D object detection results.
This figure as: png eps pdf txt gnuplot



3D object detection results.
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Bird's eye view results.
This figure as: png eps pdf txt gnuplot




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