Method

Patch Refinment [la] [Patches]


Submitted on 28 Jan. 2019 17:53 by
Johannes Lehner (JKU Linz)

Running time:0.15 s
Environment:GPU @ 2.0 Ghz

Method Description:
trained with 50/50 split

v1.0 18.12.2018
3D - 87.07 - 76.56 - 68.65
BEV - 89.97 - 86.06 - 79.42
Parameters:
Latex Bibtex:
@article{lehner2019patch,
title={Patch Refinement: Localized 3D
Object Detection},
author={Johannes Lehner and Andreas
Mitterecker and Thomas Adler and Markus
Hofmarcher and Bernhard Nessler and Sepp
Hochreiter},
journal={arXiv preprint arXiv:1910.04093},
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) 96.34 % 92.72 % 87.63 %
Car (Orientation) 96.31 % 92.57 % 87.41 %
Car (3D Detection) 88.67 % 77.20 % 71.82 %
Car (Bird's Eye View) 92.72 % 88.39 % 83.19 %
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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