Method

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


Submitted on 8 Oct. 2019 15:02 by
Shaoshuai Shi (CUHK)

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

Method Description:
Results with previous evaluation metric (submitted
on 27 Jun 2019):

Car (3D Detection) 85.94 % 77.86 % 72.00 %
Car (Bird's Eye View) 89.52 % 84.76 % 81.47 %
Cyclist (3D Detection) 78.58 % 62.73 % 57.74 %
Cyclist (Bird's Eye View) 81.91 % 68.12 % 61.92 %
Parameters:
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) 95.03 % 91.86 % 89.06 %
Car (Orientation) 95.00 % 91.73 % 88.86 %
Car (3D Detection) 87.81 % 78.49 % 73.51 %
Car (Bird's Eye View) 91.70 % 87.79 % 84.61 %
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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