Method

Detection Aware 3D Semantic Segmentation [DASS]


Submitted on 23 Nov. 2020 22:06 by
Ozan Ünal (ETH Zurich)

Running time:0.09 s
Environment:1 core @ 2.0 Ghz (Python)

Method Description:
Please refer to the paper.
Parameters:
Please refer to the paper.
Latex Bibtex:
@InProceedings{Unal_2021_WACV,
author = {Unal, Ozan and Van Gool, Luc and
Dai, Dengxin},
title = {Improving Point Cloud Semantic
Segmentation by Learning 3D Object Detection},
booktitle = {Proceedings of the IEEE/CVF
Winter Conference on Applications of Computer
Vision (WACV)},
month = {January},
year = {2021},
pages = {2950-2959}
}

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) 96.23 % 92.53 % 87.75 %
Car (Orientation) 96.20 % 92.25 % 87.26 %
Car (3D Detection) 81.85 % 72.31 % 65.99 %
Car (Bird's Eye View) 91.74 % 85.85 % 80.97 %
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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