Artificial Intelligence - Special volume on computer vision
Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Acquiring a Radiance Distribution to Superimpose Virtual Objects onto a Real Scene
IEEE Transactions on Visualization and Computer Graphics
Automatic Camera Recovery for Closed or Open Image Sequences
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Automatic line matching across views
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A linear method for reconstruction from lines and points
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Accurate Camera Calibration for Off-line, Video-Based Augmented Reality
ISMAR '02 Proceedings of the 1st International Symposium on Mixed and Augmented Reality
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Projective reconstruction is known to be an important step for 3D reconstruction in Euclidean space. In this paper, we present a new projective reconstruction algorithm based on invariant properties of the line segments in projective space: collinearity, order of contact, intersection. Points on each line segment in the image are reconstructed in projective space, and we determine the best-fit 3D line from them by Least-Median-Squares (LMedS). Our method regards the points unsatisfying collinearity as outliers, which are caused by false feature detection and tracking. In addition, both order of contact and intersection in projective space are considered. By using the points that are the orthogonal projection of outliers onto the 3D line, we iteratively obtain more precise projective matrix than the previous method.