Method

A Unified Query-based Paradigm for Point Cloud Understanding [EQ-PVRCNN]
https://github.com/dvlab-research/DeepVision3D

Submitted on 16 Nov. 2021 10:54 by
Zetong Yang (CUHK)

Running time:0.2 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
To be determined
Parameters:
To be determined
Latex Bibtex:
@inproceedings{Yang2022eqparadigm,
title = {A Unified Query-based Paradigm for Point Cloud
Understanding},
author = {Zetong Yang and Li Jiang and Yanan Sun and
Bernt Schiele and Jiaya Jia},
booktitle = {Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition},
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) 98.23 % 95.32 % 92.65 %
Car (Orientation) 98.22 % 95.20 % 92.47 %
Car (3D Detection) 90.13 % 82.01 % 77.53 %
Car (Bird's Eye View) 94.55 % 89.09 % 86.42 %
Pedestrian (Detection) 77.19 % 65.01 % 61.95 %
Pedestrian (Orientation) 43.67 % 36.49 % 34.67 %
Pedestrian (3D Detection) 55.84 % 47.02 % 42.94 %
Pedestrian (Bird's Eye View) 61.73 % 52.81 % 49.87 %
Cyclist (Detection) 89.07 % 80.37 % 74.20 %
Cyclist (Orientation) 88.92 % 80.09 % 73.79 %
Cyclist (3D Detection) 85.41 % 69.10 % 62.30 %
Cyclist (Bird's Eye View) 86.25 % 73.30 % 65.49 %
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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