Three-dimensional scene reconstruction from images

Modeling of 3D objects from image sequences is a challenging problem and has been a research topic for many years. Important theoretical and algorithmic results were achieved that allow to extract even complex 3D models of scenes from sequences of images. One recent effort has been to reduce the amount of calibration and to avoid restrictions on the camera motion. In this contribution an approach is described which achieves this gaol by coming state-of-the-art algorithms for uncalibrated projective reconstruction, self- calibration and dense correspondence matching.

[1]  Reinhard Koch,et al.  Metric 3D Surface Reconstruction from Uncalibrated Image Sequences , 1998, SMILE.

[2]  Reinhard Koch,et al.  Automatische Oberflächenmodellierung starrer dreidimensionaler Objekte aus stereoskopischen Rundum-Ansichten , 1997 .

[3]  Reinhard Koch,et al.  A simple and efficient rectification method for general motion , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[4]  Reinhard Koch,et al.  Calibration of hand-held camera sequences for plenoptic modeling , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[5]  Rajiv Gupta,et al.  Stereo from uncalibrated cameras , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Richard I. Hartley,et al.  Euclidean Reconstruction from Uncalibrated Views , 1993, Applications of Invariance in Computer Vision.

[7]  Reinhard Koch,et al.  Multi Viewpoint Stereo from Uncalibrated Video Sequences , 1998, ECCV.

[8]  Peter F. Sturm,et al.  Critical motion sequences for monocular self-calibration and uncalibrated Euclidean reconstruction , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Jitendra Malik,et al.  Modeling and Rendering Architecture from Photographs: A hybrid geometry- and image-based approach , 1996, SIGGRAPH.

[10]  Olivier D. Faugeras,et al.  Oriented Projective Geometry for Computer Vision , 1996, ECCV.

[11]  Olivier D. Faugeras,et al.  What can be seen in three dimensions with an uncalibrated stereo rig , 1992, ECCV.

[12]  O. D. Faugeras,et al.  Camera Self-Calibration: Theory and Experiments , 1992, ECCV.

[13]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[14]  Reinhard Koch,et al.  Self-Calibration and Metric Reconstruction Inspite of Varying and Unknown Intrinsic Camera Parameters , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[15]  Ingemar J. Cox,et al.  Cylindrical rectification to minimize epipolar distortion , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  KanadeTakeo,et al.  Shape and motion from image streams under orthography , 1992 .

[17]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[18]  M. Pollefeys.  Self-calibration and metric 3d reconstruction from uncalibrated image sequences , 1999 .

[19]  Lutz Falkenhagen.  Hierarchical Block-Based Disparity Estimation Considering Neighbourhood Constraints , 1997 .

[20]  Luc Van Gool,et al.  Stratified Self-Calibration with the Modulus Constraint , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Ingemar J. Cox,et al.  A Maximum Likelihood Stereo Algorithm , 1996, Comput. Vis. Image Underst..

[22]  M. Werman,et al.  Highlight and Re ection-Independent Multiresolution Textures from Image Sequences , 1997 .

[23]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[24]  Rachid Deriche,et al.  A Robust Technique for Matching two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry , 1995, Artif. Intell..