Projector calibration by "inverse camera calibration"

Authors:
Ivan Martynov;Joni-Kristian Kamarainen;Lasse Lensu
Affiliations:
Machine Vision and Pattern Recognition Laboratory, (Kouvola Unit), Lappeenranta University of Technology, Finland;Machine Vision and Pattern Recognition Laboratory, (Kouvola Unit), Lappeenranta University of Technology, Finland;Machine Vision and Pattern Recognition Laboratory, (Kouvola Unit), Lappeenranta University of Technology, Finland
Venue:
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Year:
2011

Citing 8
Cited 1

Geometric Camera Calibration Using Circular Control Points

IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Pose Estimation from Points or Lines

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision

Multiple View Geometry in Computer Vision
Autocalibration of a Projector-Camera System

IEEE Transactions on Pattern Analysis and Machine Intelligence
An empirical evaluation of factors influencing camera calibration accuracy using three publicly available techniques

Machine Vision and Applications
Accurate Camera Calibration from Multi-View Stereo and Bundle Adjustment

International Journal of Computer Vision
A state of the art in structured light patterns for surface profilometry

Pattern Recognition
A practical structured light acquisition system for point-based geometry and texture

SPBG'05 Proceedings of the Second Eurographics / IEEE VGTC conference on Point-Based Graphics

Structured light self-calibration with vanishing points

Machine Vision and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

The accuracy of 3-D reconstructions depends substantially on the accuracy of active vision system calibration. In this work, the problem of video projector calibration is solved by inverting the standard camera calibration work flow. The calibration procedure requires a single camera, which does not need to be calibrated and which is used as the sensor whether projected dots and calibration pattern landmarks, such as the checkerboard corners, coincide. The method iteratively adjusts the projected dots to coincide with the landmarks and the final coordinates are used as inputs to a camera calibration method. The otherwise slow iterative adjustment is accelerated by estimating a plane homography between the detected landmarks and the projected dots, which makes the calibration method fast.