Method

PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud (LiDAR only) [la] [PointRCNN-V1.1]
[Anonymous Submission]

Submitted on 7 Feb. 2019 18:32 by
[Anonymous Submission]

Running time:0.1 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
See the paper, trained from scratch with 50/50 split.
Update: add results of pedestrian and cyclist.
Parameters:
See the paper and wait for the code release.
Latex Bibtex:
@article{shi2018pointrcnn,
title={PointRCNN: 3D Object Proposal Generation and
Detection from Point Cloud},
author={Shi, Shaoshuai and Wang, Xiaogang and Li,
Hongsheng},
journal={arXiv preprint arXiv:1812.04244},
year={2018}
}

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) 90.74 % 89.32 % 85.73 %
Car (Orientation) 90.73 % 89.22 % 85.53 %
Car (3D Detection) 85.94 % 75.76 % 68.32 %
Car (Bird's Eye View) 89.47 % 85.68 % 79.10 %
Pedestrian (Detection) 59.05 % 50.88 % 48.46 %
Pedestrian (Orientation) 57.49 % 48.98 % 46.48 %
Pedestrian (3D Detection) 49.43 % 41.78 % 38.63 %
Pedestrian (Bird's Eye View) 55.92 % 47.53 % 44.67 %
Cyclist (Detection) 83.64 % 72.94 % 66.07 %
Cyclist (Orientation) 83.40 % 72.35 % 65.50 %
Cyclist (3D Detection) 73.93 % 59.60 % 53.59 %
Cyclist (Bird's Eye View) 81.52 % 66.77 % 60.78 %
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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