Method

Frustum PointNets for 3D Object Detection from RGB-D Data [la] [F-PointNet]
https://github.com/charlesq34/frustum-pointnets

Submitted on 15 Nov. 2017 01:28 by
Charles R. Qi (Stanford University)

Running time:0.17 s
Environment:GPU @ 3.0 Ghz (Python)

Method Description:
See the paper.
Parameters:
See the paper.
Latex Bibtex:
@article{qi2017frustum,
title={Frustum PointNets for 3D Object Detection from RGB-D Data},
author={Qi, Charles R and Liu, Wei and Wu, Chenxia and Su, Hao
and Guibas, Leonidas J},
journal={arXiv preprint arXiv:1711.08488},
year={2017}
}

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) 90.78 % 90.00 % 80.80 %
Car (3D Detection) 81.20 % 70.39 % 62.19 %
Car (Bird's Eye View) 88.70 % 84.00 % 75.33 %
Pedestrian (Detection) 87.81 % 77.25 % 74.46 %
Pedestrian (3D Detection) 51.21 % 44.89 % 40.23 %
Pedestrian (Bird's Eye View) 58.09 % 50.22 % 47.20 %
Cyclist (Detection) 84.90 % 72.25 % 65.14 %
Cyclist (3D Detection) 71.96 % 56.77 % 50.39 %
Cyclist (Bird's Eye View) 75.38 % 61.96 % 54.68 %
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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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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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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