Details
| Original language | English |
|---|---|
| Title of host publication | Computer Vision |
| Subtitle of host publication | ECCV 2014 Workshops, Proceedings |
| Publisher | Springer Verlag |
| Pages | 415-427 |
| Number of pages | 13 |
| ISBN (electronic) | 9783319161778 |
| Publication status | Published - 19 Mar 2015 |
| Event | 13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland Duration: 6 Sept 2014 → 12 Sept 2014 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 8925 |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Time-of-flight (ToF) cameras are becoming more and more popular in computer vision. In many applications 3D information delivered by a ToF camera is used, and it is very important to know the camera’s extrinsic and intrinsic parameters, as well as precise depth information. A straightforward algorithm to calibrate a ToF camera is to use a standard color camera calibration procedure [12], on the amplitude images. However, depth information delivered by ToF cameras is known to contain complex bias due to several error sources [6]. Additionally, it is desirable in many cases to determine the pose of the ToF camera relative to the other sensors used. In this work, we propose a method for joint color and ToF camera calibration, that determines extrinsic and intrinsic camera parameters and corrects depth bias. The calibration procedure requires a standard calibration board and around 20–30 images, as in case of a single color camera calibration. We evaluate the calibration quality in several experiments. The code for the calibration toolbox is made available online.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Computer Vision: ECCV 2014 Workshops, Proceedings. Springer Verlag, 2015. p. 415-427 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8925).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - On calibration of a low-cost time-of-flight camera
AU - Kuznetsova, Alina
AU - Rosenhahn, Bodo
PY - 2015/3/19
Y1 - 2015/3/19
N2 - Time-of-flight (ToF) cameras are becoming more and more popular in computer vision. In many applications 3D information delivered by a ToF camera is used, and it is very important to know the camera’s extrinsic and intrinsic parameters, as well as precise depth information. A straightforward algorithm to calibrate a ToF camera is to use a standard color camera calibration procedure [12], on the amplitude images. However, depth information delivered by ToF cameras is known to contain complex bias due to several error sources [6]. Additionally, it is desirable in many cases to determine the pose of the ToF camera relative to the other sensors used. In this work, we propose a method for joint color and ToF camera calibration, that determines extrinsic and intrinsic camera parameters and corrects depth bias. The calibration procedure requires a standard calibration board and around 20–30 images, as in case of a single color camera calibration. We evaluate the calibration quality in several experiments. The code for the calibration toolbox is made available online.
AB - Time-of-flight (ToF) cameras are becoming more and more popular in computer vision. In many applications 3D information delivered by a ToF camera is used, and it is very important to know the camera’s extrinsic and intrinsic parameters, as well as precise depth information. A straightforward algorithm to calibrate a ToF camera is to use a standard color camera calibration procedure [12], on the amplitude images. However, depth information delivered by ToF cameras is known to contain complex bias due to several error sources [6]. Additionally, it is desirable in many cases to determine the pose of the ToF camera relative to the other sensors used. In this work, we propose a method for joint color and ToF camera calibration, that determines extrinsic and intrinsic camera parameters and corrects depth bias. The calibration procedure requires a standard calibration board and around 20–30 images, as in case of a single color camera calibration. We evaluate the calibration quality in several experiments. The code for the calibration toolbox is made available online.
UR - http://www.scopus.com/inward/record.url?scp=84925308087&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-16178-5_29
DO - 10.1007/978-3-319-16178-5_29
M3 - Conference contribution
AN - SCOPUS:84925308087
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 415
EP - 427
BT - Computer Vision
PB - Springer Verlag
T2 - 13th European Conference on Computer Vision, ECCV 2014
Y2 - 6 September 2014 through 12 September 2014
ER -