Method

Structured Color Image Representation Based 3D Object Detection Network from Point Clouds[la] [SCIR-Net]


Submitted on 8 Mar. 2021 13:20 by
Qingdong He (UESTC)

Running time:0.03 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@inproceedings{he2022scir,
title={SCIR-Net: Structured Color Image
Representation Based 3D Object Detection Network
from Point Clouds},
author={He, Qingdong and Zeng, Hao and Zeng, Yi and
Liu, Yijun},
booktitle={AAAI},
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) 96.15 % 94.76 % 91.99 %
Car (Orientation) 96.11 % 94.55 % 91.68 %
Car (3D Detection) 87.53 % 80.62 % 76.00 %
Car (Bird's Eye View) 92.11 % 90.04 % 85.63 %
Pedestrian (Detection) 68.18 % 58.37 % 55.78 %
Pedestrian (Orientation) 62.55 % 52.45 % 49.62 %
Pedestrian (3D Detection) 49.23 % 40.95 % 38.47 %
Pedestrian (Bird's Eye View) 53.47 % 46.76 % 43.72 %
Cyclist (Detection) 84.92 % 74.88 % 68.13 %
Cyclist (Orientation) 84.63 % 74.50 % 67.74 %
Cyclist (3D Detection) 76.32 % 60.89 % 54.48 %
Cyclist (Bird's Eye View) 80.95 % 67.17 % 60.55 %
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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