Generalized Image Acquisition and Analysis

A Mathematical Model and Calibration Procedure for Galvanometric Laser Scanning Systems

Laser galvanometric scanning systems are commonly used in various fields such as three dimensional scanning, medical imaging, material processing, measurement devices and laser display systems. The systems of such kind suffer from distortions. On top of that they do not have a center of projection, which makes it impossible to use common projector calibration procedures. The paper presents a novel mathematical model to predict the image distortions caused by galvanometric mirror scanning systems. In addition, we describe a calibration procedure for recovering its intrinsic and extrinsic parameters.

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Three-Dimensional Kaleidoscopic Imaging

Ilya Reshetouski, Alkhazur Manakov, Hans-Peter Seidel, Ivo Ihrke
In: Proceedings of CVPR 2011 (oral).



Abstract

Three-dimensional kaleidoscopic imaging, a promising alternative for recording multi-view imagery. The main limitation of multi-view reconstruction techniques is the limited number of views that are available from multi-camera systems, especially for dynamic scenes. Our new system is based on imaging an object inside a kaleidoscopic mirror system. We show that this approach can generate a large number of high-quality views well distributed over the hemisphere surrounding the object in a single shot. In comparison to existing multi-view systems, our method offers a number of advantages: it is possible to operate with a single camera, the individual views are perfectly synchronized, and they have the same radiometric and colorimetric properties. We describe the setup both theoretically, and provide methods for a practical implementation. Enabling interfacing to standard multi-view algorithms for further processing is an important goal of our techniques.
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Bibtex

@inproceedings{Reshetouski11:TKI,
author = {Ilya Reshetouski and Alkhazur Manakov and Hans-Peter Seidel and Ivo Ihrke},
title = {Three-Dimensional Kaleidoscopic Imaging},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = 2011,
pages = {353--360},
}
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