Computer vision core: 3D vision

3D computer vision has been one of the core research areas in CMVS since early 1990’s. This research has resulted in many novel methods and software tools that have been widely used in the research community and companies.

During the last few years our focus has been in 3D computer vision techniques that enable more advanced features in augmented reality applications, where real and virtual objects co-exist in the same environment. Wearable computers such as Google Glass have created a strong demand for such technology. One of the fundamental problems investigated in our work is accurate localization of the user with respect to the environment. Other important research problems include estimation of the 3D scene structure and building a dense 3D model from multiple images.

 

Camera calibration

Geometry is an important aspect of computer vision. The laws of geometry and optics describe how the three-dimensional world is imaged on the camera sensor and, hence, an understanding of imaging geometry is important for the development of automatic image analysis methods. Geometric camera calibration is an important topic because it is a prerequisite for imagebased metric 3D measurements, and we have a strong expertise in that field.
Camera calibration toolboxes:

   

References

Heikkilä, J, "Geometric Camera Calibration Using Circular Control Points", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 10, pp. 1066-1077, Oct 2000.
 
Juho Kannala and Sami S. Brandt. "A generic camera model and calibration method for conventional, wide-angle and fish-eye lenses". IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 8, August 2006.
 
Juho Kannala, Janne Heikkilä and Sami S. Brandt. "Geometric camera calibration". Wiley Encyclopedia of Computer Science and Engineering, 2008.
 
Kannala, Juho, Sami S. Brandt, and Janne Heikkilä. "Self-calibration of central cameras from point correspondences by minimizing angular error." International Conference on Computer Vision and Computer Graphics. Springer Berlin Heidelberg, 2008.
 
Herrera Castro D, Kannala J & Heikkilä J, "Joint depth and color camera calibration with distortion correction". IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(10):2058-2064.
 
Herrera Castro D, Kannala J & Heikkilä J, "Forget the checkerboard: practical self-calibration using a planar scene". Proc. IEEE Winter Conference on Applications of Computer Vision (WACV 2016)

 

3D Reconstruction

Automatically acquiring a three-dimensional model of a scene from multiple photographic images is an important research area in computer vision. During the last few years, we have also focused on 3D modeling from point clouds produced with image-based 3D reconstruction techniques.