Parallel Processing of Range Data Merging
This chapter describes a volumetric view-merging algorithm that generates a consensus surface of an object from its range images. Our original method merges a set of range images into a volumetric implicit-surface representation, which is converted to a surface mesh by using a variant of the marching-cubes algorithm. We propose a method that increases the computation and memory efficiency for computing signed distances and the method of parallel computing on a PC cluster. Since our method permits a reduction in the data amount allocated in memory, the closest point is searched efficiently; this allows us to increase the number of parallel traversals and to reduce the computation time.
In this chapter, we describe the following two algorithms which are complementary in terms of the efficiency of CPUs and memory usage: distributed allocation of range data and parallel traversal of partial octrees. By adjusting them according to the system specifications, we can build the model efficiently by a PC cluster. We have implemented this system and evaluated its performance.
KeywordsParallel Processing Close Point Range Data Signed Distance Range Image
Unable to display preview. Download preview PDF.
- D. Bartz and W. Straer. Parallel construction and isosurface extraction of recursive tree structures. In Proceedings of WSCG’98, Vol. III, Plzen, 1998.Google Scholar
- Brian Curless and Marc Levoy. A volumetric method for building complex models from range images. In Proc. SIGGRAPH’96, pp. 303-312. ACM, 1996.Google Scholar
- A. Hilton, A.J. Stoddart, J. Illingworth, and T. Windeatt. Reliable surface reconstruction from multiple range images. In Proceedings of European Conference on Computer Vision, pp. 117-126, Springer-Verlag, 1996.Google Scholar
- H. Hoppe, T. DeRose, T. Duchamp, J.A. McDonald, and W. Stuetzle. Surface reconstruction from unorganized points. In Proc. SIGGRAPH’92, pp. 71-78. ACM, 1992.Google Scholar
- W. Lorensen and H. Cline. Marching cubes: a high resolution 3d surface construction algorithm. In Proc. SIGGRAPH’87, pp. 163-170. ACM, 1987.Google Scholar
- P. Mackerras. A fast parallel marching-cubes implementation on the fujitsu ap1000. Technical report, Australian National University, TR-CS-92-10, 1992.Google Scholar
- D. J. R. Meagher. The octree encoding method for efficient solid modeling. PhD thesis, Rensselaer Polytechnic Institute, 1980.Google Scholar
- Daisuke Miyazaki, Takeshi Ooishi, Taku Nishikawa, Ryusuke Sagawa, Ko Nishino, Takashi Tomomatsu, Yutaka Takase, and Katsushi Ikeuchi. The great buddha project: Modelling cultural heritage through observation. In Proceedings of 6th International Conference on Virtual Systems and MultiMedia, pp. 138-145, Gifu, 2000.Google Scholar
- M. Rutishauser, M. Stricker, and M. Trobina. Merging range images of arbitararily shaped objects. In Proceedings of 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 573-580, June 1994.Google Scholar
- Greg Turk and Marc Levoy. Zippered polygon meshes from range images. In Proceedings of SIGGRAPH’94, pp. 311-318. ACM, 1994.Google Scholar
- M.D. Wheeler, Y. Sato, and K. Ikeuchi. Consensus surfaces for modeling 3d objects from multiple range images. In Proc. International Conference on Computer Vision, January 1998.Google Scholar