Why is the task important?
- Perspective projection, occlusions and outliers are often present.
- Many images are often available -> a technique treating all data uniformly is needed.
- It can be used on both wide base-line stereo and sequences.
- It has automatical outlier detection.
- It is based on a factorization method -> linear solution is computed fast; subsequent non-linear bundle adjustment is optional.
- Outliers are rejected using a simplified RANSAC technique. Significant speed-up is achieved using reconstructions from partial data.
- Notation: … outlier, … occlusion, … projective depth ( )
Presentation given at the Pattern Recognition and Computer Vision Colloquium, Summer 2002 [pdf]
Structure from many perspective images with occlusions. Daniel Martinec and Tomáš Pajdla. In Proceedings of the European Conference on Computer Vision (ECCV), pp. 355-369, Springer-Verlag, May 2002. [pdf], [poster]
Outlier Detection for Factorization-Based Reconstruction from Perspective Images with Occlusions. Daniel Martinec and Tomáš Pajdla. In Proceedings of the Photogrammetric Computer Vision (PCV), pp. 161-164, Inst. f. Computer Graphics and Vision, TU-Graz, September 2002. [pdf], [poster]
Automatic Factorization-Based Reconstruction from Perspective Images with Occlusions and Outliers. Daniel Martinec and Tomáš Pajdla. In Proceedings of the 8th Computer Vision Winter Workshop (CVWW), pp. 147-152, Czech Pattern Recognition Society, Prague, February 2003. [pdf]