Motion - Stereo Integration for Depth Estimation

Depth extraction with a mobile stereo system is described. The stereo setup is precalibrated, but the system extracts its own motion. Emphasis lies on the integration of the motion and stereo cues. It is guided by the relative confidence that the system has in these cues. This weighing is fine-grained in that it is determined for every pixel at every iteration. Reliable information spreads fast at the expense of less reliable data, both in terms of spatial communication and in terms of exchange between cues. The resulting system can handle large displacements, depth discontinuities and occlusions. Experimental results corroborate the viability of the approach.

[1]  Olivier D. Faugeras,et al.  Motion of an uncalibrated stereo rig: self-calibration and metric reconstruction , 1996, IEEE Trans. Robotics Autom..

[2]  M. Vergauwen,et al.  A hierarchical stereo algorithm using dynamic programming , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[3]  Max A. Viergever,et al.  Efficient and reliable schemes for nonlinear diffusion filtering , 1998, IEEE Trans. Image Process..

[4]  Jayant Shah A nonlinear diffusion model for discontinuous disparity and half-occlusions in stereo , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[5]  I. Reid,et al.  Metric calibration of a stereo rig , 1995, Proceedings IEEE Workshop on Representation of Visual Scenes (In Conjunction with ICCV'95).

[6]  Olivier Faugeras,et al.  Motion of an uncalibrated stereo rig: self-calibration and metric reconstruction , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[7]  T. Poggio,et al.  A computational theory of human stereo vision , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[8]  Luc Van Gool,et al.  Determination of Optical Flow and its Discontinuities using Non-Linear Diffusion , 1994, ECCV.

[9]  Kanad K. Biswas,et al.  A cooperative integration of stereopsis and optic flow computation , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[10]  Harpreet S. Sawhney,et al.  Correlation-based estimation of ego-motion and structure from motion and stereo , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[11]  Gideon P. Stein,et al.  Lens distortion calibration using point correspondences , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Amnon Shashua,et al.  Direct estimation of motion and extended scene structure from a moving stereo rig , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[13]  Michal Irani,et al.  Multi-frame optical flow estimation using subspace constraints , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Kanad K. Biswas,et al.  Cooperative integration of stereopsis and optic flow computation , 1995 .

[15]  Ronald Chung,et al.  Stereo-Motion with Stereo and Motion in Complement , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Joachim Weickert,et al.  Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint , 2001, Journal of Mathematical Imaging and Vision.

[17]  Allan D. Jepson,et al.  Subspace methods for recovering rigid motion I: Algorithm and implementation , 2004, International Journal of Computer Vision.

[18]  Rachid Deriche,et al.  Dense Disparity Map Estimation Respecting Image Discontinuities: A PDE and Scale-Space BasedApproach , 2002, MVA.

[19]  Bart M. ter Haar Romeny,et al.  Geometry-Driven Diffusion in Computer Vision , 1994, Computational Imaging and Vision.

[20]  F. Dornaika,et al.  Stereo correspondence from motion correspondence , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).