Method

3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud [la] [MMLab-PartA^2]


Submitted on 27 Jun. 2019 08:45 by
Shaoshuai Shi (CUHK)

Running time:0.08 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
See the paper.
Parameters:
See the paper.
Latex Bibtex:
@article{shi2019part,
title={Part-A^2 Net: 3D Part-Aware and
Aggregation Neural Network for Object Detection
from Point Cloud},
author={Shi, Shaoshuai and Wang, Zhe and Wang,
Xiaogang and Li, Hongsheng},
journal={arXiv preprint arXiv:1907.03670},
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) 90.60 % 89.34 % 87.57 %
Car (Orientation) 90.41 % 88.98 % 87.08 %
Car (3D Detection) 85.94 % 77.86 % 72.00 %
Car (Bird's Eye View) 89.52 % 84.76 % 81.47 %
Cyclist (Detection) 85.54 % 77.48 % 70.35 %
Cyclist (Orientation) 85.37 % 76.74 % 69.63 %
Cyclist (3D Detection) 78.58 % 62.73 % 57.74 %
Cyclist (Bird's Eye View) 81.91 % 68.12 % 61.92 %
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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