Algorithm for analysing optical flow based on the least-squares method
Image and Vision Computing
Active, optical range imaging sensors
Machine Vision and Applications
Stochastic perturbation theory
SIAM Review
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
International Journal of Computer Vision - Special issue on image-based servoing
Regularized Bundle-Adjustment to Model Heads from Image Sequences without Calibration Data
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Optimization Criteria and Geometric Algorithms for Motion and Structure Estimation
International Journal of Computer Vision
Handbook of Computer Vision and Applications with Cdrom
Handbook of Computer Vision and Applications with Cdrom
A Theory of Shape by Space Carving
International Journal of Computer Vision - Special issue on Genomic Signal Processing
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Spatio-Temporal Stereo Using Multi-Resolution Subdivision Surfaces
International Journal of Computer Vision
Changes in Surface Convexity and Topology Caused by Distortions of Stereoscopic Visual Space
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Catadioptric Omnidirectional Camera
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Comparison of Approaches to Egomotion Computation
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Stochastic Approximation and Rate-Distortion Analysis for Robust Structure and Motion Estimation
International Journal of Computer Vision
Eyes from Eyes: New Cameras for Structure from Motion
OMNIVIS '02 Proceedings of the Third Workshop on Omnidirectional Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Eye Design in the Plenoptic Space of Light Rays
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Invitation to 3-D Vision: From Images to Geometric Models
An Invitation to 3-D Vision: From Images to Geometric Models
Rank Conditions on the Multiple-View Matrix
International Journal of Computer Vision
A pyramid approach to subpixel registration based on intensity
IEEE Transactions on Image Processing
Information fusion for multi-camera and multi-body structure and motion
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
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The view-independent visualization of 3D scenes is most often based on rendering accurate 3D models or utilizes image-based rendering techniques. To compute the 3D structure of a scene from a moving vision sensor or to use image-based rendering approaches, we need to be able to estimate the motion of the sensor from the recorded image information with high accuracy, a problem that has been well-studied. In this work, we investigate the relationship between camera design and our ability to perform accurate 3D photography, by examining the influence of camera design on the estimation of the motion and structure of a scene from video data. By relating the differential structure of the time varying plenoptic function to different known and new camera designs, we can establish a hierarchy of cameras based upon the stability and complexity of the computations necessary to estimate structure and motion. At the low end of this hierarchy is the standard planar pinhole camera for which the structure from motion problem is non-linear and ill-posed. At the high end is a camera, which we call the full field of view polydioptric camera, for which the motion estimation problem can be solved independently of the depth of the scene which leads to fast and robust algorithms for 3D Photography. In between are multiple view cameras with a large field of view which we have built, as well as omni-directional sensors.