Advanced

Robust Online 3D Reconstruction Combining a Depth Sensor and Sparse Feature Points

Bylow, Erik LU ; Olsson, Carl LU and Kahl, Fredrik LU (2017) 2016 23rd International Conference on Pattern Recognition (ICPR 2016) In Pattern Recognition (ICPR), 2016 23rd International Conference on p.3709-3714
Abstract
Online 3D reconstruction has been an active research area for a long time. Since the release of the Microsoft Kinect Camera and publication of KinectFusion [11] attention has been drawn how to acquire dense models in real-time. In this paper we present a method to make online 3D reconstruction which increases robustness for scenes with little structure information and little texture information. It is shown empirically that our proposed method also increases robustness when the distance between the camera positions becomes larger than what is commonly assumed. Quantitative and qualitative results suggest that this approach can handle situations where other well-known methods fail. This is important in, for example, robotics applications... (More)
Online 3D reconstruction has been an active research area for a long time. Since the release of the Microsoft Kinect Camera and publication of KinectFusion [11] attention has been drawn how to acquire dense models in real-time. In this paper we present a method to make online 3D reconstruction which increases robustness for scenes with little structure information and little texture information. It is shown empirically that our proposed method also increases robustness when the distance between the camera positions becomes larger than what is commonly assumed. Quantitative and qualitative results suggest that this approach can handle situations where other well-known methods fail. This is important in, for example, robotics applications like when the camera position and the 3D model must be created online in real-time. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Pattern Recognition (ICPR), 2016 23rd International Conference on
pages
6 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
2016 23rd International Conference on Pattern Recognition (ICPR 2016)
external identifiers
  • scopus:85019134505
ISBN
978-1-5090-4847-2
DOI
10.1109/ICPR.2016.7900211
language
English
LU publication?
yes
id
052b8cdb-edcf-42bf-8870-f54dffc39249
date added to LUP
2017-03-17 12:53:32
date last changed
2018-03-01 14:51:46
@inproceedings{052b8cdb-edcf-42bf-8870-f54dffc39249,
  abstract     = {Online 3D reconstruction has been an active research area for a long time. Since the release of the Microsoft Kinect Camera and publication of KinectFusion [11] attention has been drawn how to acquire dense models in real-time. In this paper we present a method to make online 3D reconstruction which increases robustness for scenes with little structure information and little texture information. It is shown empirically that our proposed method also increases robustness when the distance between the camera positions becomes larger than what is commonly assumed. Quantitative and qualitative results suggest that this approach can handle situations where other well-known methods fail. This is important in, for example, robotics applications like when the camera position and the 3D model must be created online in real-time.},
  author       = {Bylow, Erik and Olsson, Carl and Kahl, Fredrik},
  booktitle    = { Pattern Recognition (ICPR), 2016 23rd International Conference on},
  isbn         = {978-1-5090-4847-2 },
  language     = {eng},
  month        = {04},
  pages        = {3709--3714},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Robust Online 3D Reconstruction Combining a Depth Sensor and Sparse Feature Points},
  url          = {http://dx.doi.org/10.1109/ICPR.2016.7900211},
  year         = {2017},
}