Method

Point Density-aware Voxels [PDV]
https://github.com/TRAILab/PDV

Submitted on 10 Nov. 2021 04:58 by
Jordan Sir Kwang Hu (University of Toronto)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
TBA
Parameters:
TBA
Latex Bibtex:
@inproceedings{PDV,
title={Point Density-Aware Voxels for LiDAR 3D Object Detection},
author={Jordan S. K. Hu and
Tianshu Kuai and
Steven L. Waslander},
booktitle={CVPR},
year={2022}
}

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) 96.07 % 95.00 % 92.44 %
Car (Orientation) 96.06 % 94.91 % 92.30 %
Car (3D Detection) 90.43 % 81.86 % 77.36 %
Car (Bird's Eye View) 94.56 % 90.48 % 86.23 %
Pedestrian (Detection) 65.94 % 57.34 % 54.21 %
Pedestrian (Orientation) 63.43 % 54.08 % 50.75 %
Pedestrian (3D Detection) 47.80 % 40.56 % 38.46 %
Pedestrian (Bird's Eye View) 51.95 % 45.45 % 43.33 %
Cyclist (Detection) 88.76 % 79.84 % 73.04 %
Cyclist (Orientation) 88.66 % 79.34 % 72.56 %
Cyclist (3D Detection) 83.04 % 67.81 % 60.46 %
Cyclist (Bird's Eye View) 85.54 % 71.31 % 64.40 %
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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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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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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