Method

Detection Aware 3D Semantic Segmentation [DASS]


Submitted on 23 Nov. 2020 22:06 by
Ozan Unal (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:
@misc{unal2020improving,
title={Improving Point Cloud Semantic
Segmentation by Learning 3D Object Detection},
author={Ozan Unal and Luc Van Gool and
Dengxin Dai},
year={2020},
eprint={2009.10569},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

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.
This figure as: png eps pdf txt gnuplot



Orientation estimation results.
This figure as: png eps pdf txt gnuplot



3D object detection results.
This figure as: png eps pdf txt gnuplot



Bird's eye view results.
This figure as: png eps pdf txt gnuplot




eXTReMe Tracker