Marco Filax, Frank Ortmeier: VIOL: Viewpoint Invariant Object Localizator - Viewpoint Invariant Planar Features in Man-Made Environments. In: Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), S. 581-588, 2018, ISBN: 978-989-758-290-5.

Abstract

Object detection is one of the fundamental issues in computer vision. The established methods, rely on different feature descriptors to determine correspondences between significant image points. However, they do not provide reliable results, especially for extreme viewpoint changes. This is because feature descriptors do not adhere to the projective distortion introduced with an extreme viewpoint change.
Different approaches have been proposed to lower this hurdle, e.g., by randomly sampling multiple virtual viewpoints. However, these methods are either computationally intensive or impose strong assumptions of the environment.
In this paper, we propose an algorithm to detect corresponding quasi-planar objects in man-made environments.
We make use of the observation that these environments typically contain rectangular structures.
We exploit the information gathered from a depth sensor to detect planar regions.
With these, we unwrap the projective distortion, by transforming the planar patch into a fronto-parallel view.
We demonstrate the feasibility and capabilities of our approach in a real-world scenario: a supermarket.

BibTeX (Download)

@inproceedings{visapp18,
title = {VIOL: Viewpoint Invariant Object Localizator - Viewpoint Invariant Planar Features in Man-Made Environments},
author = {Marco Filax and Frank Ortmeier},
doi = {10.5220/0006624005810588},
isbn = {978-989-758-290-5},
year  = {2018},
date = {2018-01-01},
booktitle = {Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)},
pages = {581-588},
abstract = {Object detection is one of the fundamental issues in computer vision. The established methods, rely on different feature descriptors to determine correspondences between significant image points. However, they do not provide reliable results, especially for extreme viewpoint changes.  This is because feature descriptors do not adhere to the projective distortion introduced with an extreme viewpoint change. 
Different approaches have been proposed to lower this hurdle, e.g., by randomly sampling multiple virtual viewpoints.  However, these methods are either computationally intensive or impose strong assumptions of the environment.
In this paper, we propose an algorithm to detect corresponding quasi-planar objects in man-made environments. 
We make use of the observation that these environments typically contain rectangular structures.
We exploit the information gathered from a depth sensor to detect planar regions. 
With these, we unwrap the projective distortion, by transforming the planar patch into a fronto-parallel view.
We demonstrate the feasibility and capabilities of our approach in a real-world scenario: a supermarket.},
keywords = {Augmented Reality, Fine-Grained Recognition, VIOL},
pubstate = {published},
tppubtype = {inproceedings}
}