Integration of Multiple Range Maps through Consistency Processing
This paper presents a method for modeling the surfaces of some 3D scene from a set of registered range maps. The integration of range maps into a unique accurate representation is made tricky mainly because of the presence of noise in the viewpoints positions and in the range estimates. In the present case, the scene is captured by a CCD camera system and the depth maps are estimated by a stereovision technique. This approach makes the problem of integration particularly thorny. In fact, the range maps are generally redundant but corrupted by noise and not always coherent with each other. The integration method presented in this paper is based on a fundamental principle: whatever the scene is, the range maps must be consistent with each other. This principle is used as a constraint to discard noise and increase the 3D data accuracy and to identify and remove the redundancies leading to a minimal accurate representation. This phase is realized through the detection of inconsistencies between the range maps of the different viewpoints, the identification and the removal of the most inconsistent points, and the fusion of the remaining redundant points. The process is repeated until the depth maps are coherent with each other. Finally, the facet model is built by incrementally integrating the coherent depth maps. This system is independent of the depth estimation part and can process any set of depth maps of any scene.
Unable to display preview. Download preview PDF.
- 1.M. Rutishauser, M. Stricker and M. Trobina.: Merging range images of arbitrarily shaped objects, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1994.Google Scholar
- 2.G. Turk and M. Levoy.: Zippered polygon meshes from range images, Proceedings of SIGGRAPH’94, July 1994.Google Scholar
- 3.M. Soucy and D. Laurendeau.: A general surface approach to the integration of a set of range views, IEEE PAMI, vol.17, no 4, April 1995.Google Scholar
- 4.R. Pito.: Mesh integration based on co-measurements, Proceedings IEEE ICIP, 96.Google Scholar
- 5.K. Pulli et al.: Robust meshes from multiple range maps, Proceedings of the international conference on recent advances in 3D Digital imaging and modeling, Ottawa, 12–15 May 1997.Google Scholar
- 6.A. Hilton, A. Stoddart, J. Illingworth and T. Windeatt: Reliable surface reconstruction from multiple range images, Proceedings of the 4th European Conference on Computer Vision, pages 117–126, Springer-Verlag, 1996.Google Scholar
- 7.M. Wheeler, Y. Sato and K. Ikeuchi: Consensus surfaces for modeling 3D objects from multiple range images, DARPA Image Understanding Workshop, New Orleans, Louisiana, 1997.Google Scholar
- 8.H. Delingette, M. Hebert, K. Ikeuchi.: Shape representation and image segmentation using deformable surfaces, Journal of Image and Vision Computing, 10(3):132–144, April 1992.Google Scholar
- 9.B. Hotz, Z. Zhang and P. Fua.: Incremental construction of local DEM for an autonomous planetary rover, Workshop on Computer Vision for Space Applications, Antibes, Sept. 1993.Google Scholar
- 10.P. Fua and P. Sander.: Segmenting unstructured 3D points into surfaces, ECCV 92.Google Scholar
- 11.Ph. Robert, F. Ogor.: Joint estimation of depth maps and camera motion in the construction of 3D models from a mobile camera, in European workshop on combined real and synthetic image processing for broadcast and video production, 23–24 Nov. 1994. Proc. by Springer Verlag Ed.Google Scholar
- 13.D. Minaud and Ph. Robert.: Construction of 3D models from a mobile camera, IMAGE’COM 96, Bordeaux, 20–22 May 1996.Google Scholar
- 14.D. Minaud and Ph. Robert.: Construction of 3D models from a mobile camera, Integration of a set of depth maps, International workshop on synthetic-natural hybrid coding and three-dimensional imaging (IWSNHC3DI’97), Rhodes (Greece), 5–9 September 1997.Google Scholar