What is computed by structure from motion algorithms?
In the literature we find two classes of algorithms which, on the basis of two views of a scene, recover the rigid transformation between the views and subsequently the structure of the scene. The first class contains techniques which require knowledge of the correspondence or the motion field between the images and are based on the epipolar constraint. The second class contains so-called direct algorithms which require knowledge about the value of the flow in one direction only and are based on the positive depth constraint. Algorithms in the first class achieve the solution by minimizing a function representing deviation from the epipolar constraint while direct algorithms find the 3D motion that, when used to estimate depth, produces a minimum number of negative depth values. This paper presents a stability analysis of both classes of algorithms. The formulation is such that it allows comparison of the robustness of algorithms in the two classes as well as within each class. Specifically, a general statistical model is employed to express the functions which measure the deviation from the epipolar constraint and the number of negative depth values, and these functions are studied with regard to their topographic structure, specifically as regards the errors in the 3D motion parameters at the places representing the minima of the functions. The analysis shows that for algorithms in both classes which estimate all motion parameters simultaneously, the obtained solution has an error such that the projections of the translational and rotational errors on the image plane are perpendicular to each other. Furthermore, the estimated projection of the translation on the image lies on a line through the origin and the projection of the real translation.
- 1.G. Adiv. Determining 3D motion and structure from optical flow generated by several moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7:384–401, 1985.Google Scholar
- 3.A. Bruss and B. K. P. Horn. Passive navigation. Computer Vision, Graphics, and Image Processing, 21:3–20, 1983.Google Scholar
- 5.K. Daniilidis. On the Error Sensitivity in the Recovery of Object Descriptions. PhD thesis, Department of Informatics, University of Karlsruhe, Germany, 1992. In German.Google Scholar
- 6.K. Daniilidis and M. E. Spetsakis. Understanding noise sensitivity in structure from motion. In Y. Aloimonos, editor, Visual Navigation: From Biological Systems to Unmanned Ground Vehicles, chapter 4. Lawrence Erlbaum Associates, Hillsdale, NJ, 1997.Google Scholar
- 8.C. Fermüller and Y. Aloimonos. What is computed by structure from motion algorithms? Technical Report CAR-TR-863, Center for Automation Research, University of Maryland, 1997.Google Scholar
- 11.B. K. P. Horn and E. J. Weldon. Computationally efficient methods for recovering translational motion. In Proc. International Conference on Computer Vision, pages 2–11, 1987.Google Scholar
- 12.A. D. Jepson and D. J. Heeger. Subspace methods for recovering rigid motion II: Theory. Technical Report RBCV-TR-90-36, University of Toronto, 1990.Google Scholar
- 18.S. J. Maybank. A Theoretical Study of Optical Flow. PhD thesis, University of London, England, 1987.Google Scholar
- 20.K. Prazdny. Determining instantaneous direction of motion from optical flow generated by a curvilinear moving observer. Computer Vision, Graphics, and Image Processing, 17:238–248, 1981.Google Scholar
- 22.M. E. Spetsakis. Models of statistical visual motion estimation. Computer Vision, Graphics, and Image Processing, 60:300–312, 1994.Google Scholar
- 23.J. I. Thomas, A. Hanson, and J. Oliensis. Understanding noise: The critical role of motion error in scene reconstruction. In Proc. DARPA Image Understanding Workshop, pages 691–695, 1993.Google Scholar