Method

Pseudo-Lidar[st] [Pseudo-Lidar]
https://github.com/mileyan/pseudo_lidar

Submitted on 11 Feb. 2020 19:09 by
Xiangyu Chen (University of California berkeley)

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

Method Description:
Pseudo-LiDAR from Visual Depth Estimation: Bridging the
Gap in 3D Object Detection for Autonomous Driving
Parameters:
https://github.com/mileyan/pseudo_lidar
Latex Bibtex:
@InProceedings{Wang_2019_CVPR,
author = {Wang, Yan and Chao, Wei-Lun and Garg, Divyansh
and Hariharan, Bharath and Campbell, Mark and
Weinberger, Kilian Q.},
title = {Pseudo-LiDAR From Visual Depth Estimation:
Bridging the Gap in 3D Object Detection for Autonomous
Driving},
booktitle = {The IEEE Conference on Computer Vision and
Pattern Recognition (CVPR)},
month = {June},
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) 85.40 % 67.79 % 58.50 %
Car (3D Detection) 54.53 % 34.05 % 28.25 %
Car (Bird's Eye View) 67.30 % 45.00 % 38.40 %
This table as LaTeX

2D object detection results.
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3D object detection results.
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Bird's eye view results.
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