dc.contributor.author | Houston, Ben | |
dc.contributor.author | Nielsen, Michael B. | |
dc.contributor.author | Batty, Christopher | |
dc.contributor.author | Nilsson, Ola | |
dc.contributor.author | Museth, Ken | |
dc.date.accessioned | 2021-02-03 17:45:09 (GMT) | |
dc.date.available | 2021-02-03 17:45:09 (GMT) | |
dc.date.issued | 2006-01 | |
dc.identifier.uri | https://doi.org/10.1145/1122501.1122508 | |
dc.identifier.uri | http://hdl.handle.net/10012/16789 | |
dc.description | Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 1515 Broadway, New York, NY 10036 USA, fax: +1 (212) 869-0481, or permissions@acm.org. ⃝c 2006 ACM 0730-0301/06/0100-0151 $5.00 | en |
dc.description.abstract | This article introduces the Hierarchical Run-Length Encoded (H-RLE) Level Set data structure. This novel data structure combines the best features of the DT-Grid (of Nielsen and Museth [2004]) and the RLE Sparse Level Set (of Houston et al. [2004]) to provide both optimal efficiency and extreme versatility. In brief, the H-RLE level set employs an RLE in a dimensionally recursive fashion. The RLE scheme allows the compact storage of sequential nonnarrowband regions while the dimensionally recursive encoding along each axis efficiently compacts nonnarrowband planes and volumes. Consequently, this new structure can store and process level sets with effective voxel resolutions exceeding 5000 × 3000 × 3000 (45 billion voxels) on commodity PCs with only 1 GB of memory. This article, besides introducing the H-RLE level set data structure and its efficient core algorithms, also describes numerous applications that have benefited from our use of this structure: our unified implicit object representation, efficient and robust mesh to level set conversion, rapid ray tracing, level set metamorphosis, collision detection, and fully sparse fluid simulation (including RLE vector and matrix representations.) Our comparisons of the popular octree level set and Peng level set structures to the H-RLE level set indicate that the latter is superior in both narrowband sequential access speed and overall memory usage. | en |
dc.description.sponsorship | Ben Houston and Christopher Batty were supported in part by National Research Council Industrial Research Assistance Program Grant #482564, and Michael B. Nielsen was partly supported by Center for Interactive Spaces under ISIS Katrinebjerg, Aarhus. Ken Museth would also like to acknowledge support from the Swedish Research Council (Grant #617-2004-5017) as well as research donations from Alias. This 2-year effort would not have been possible without the kind support of Frantic Films. Authors’ addresses: B. Houston: Exocortex Technologies, 6405 Sugar Creek Way, Ottawa, Ont., Canada, K1C 1X9; email; ben@exocortex.org; M. B. Nielsen, O. Nilsson, and K. Museth: Department of Science and Technology, Linko ̈ping Institute of Technology, Linko ̈ping University, 601 74 Norrko ̈ping, Sweden; email: bang@daimi.au.dk, olani@itn.liu.se, museth@acm.org; C. Batty: UBC Department of Computer Science, ICICS/CS, 201-2366 Main Mall, Vancouver, BC, Canada, V6T 1Z4; email: cbatty@cs.ubc.ca. | en |
dc.language.iso | en | en |
dc.publisher | Association for Computing Machinery | en |
dc.relation.ispartofseries | ACM Transactions on Graphics; | |
dc.relation.uri | http://www.cs.ubc.ca/labs/imager/tr/2006/Batty_HRLE/ | en |
dc.subject | algorithms | en |
dc.subject | performance | en |
dc.subject | level set methods | en |
dc.subject | implicit surfaces | en |
dc.subject | deformable surfaces | en |
dc.subject | adaptive distance fields | en |
dc.subject | computational fluid dynamics | en |
dc.subject | geometric modeling | en |
dc.subject | shape | en |
dc.subject | morphology | en |
dc.subject | mesh scan conversion | en |
dc.title | Hierarchical RLE level set: A compact and versatile deformable surface representation | en |
dc.type | Article | en |
dcterms.bibliographicCitation | Ben Houston, Michael B. Nielsen, Christopher Batty, Ola Nilsson, and Ken Museth. 2006. Hierarchical RLE level set: A compact and versatile deformable surface representation. ACM Trans. Graph. 25, 1 (January 2006), 151–175. DOI:https://doi.org/10.1145/1122501.1122508 | en |
uws.contributor.affiliation1 | Faculty of Mathematics | en |
uws.contributor.affiliation2 | David R. Cheriton School of Computer Science | en |
uws.typeOfResource | Text | en |
uws.peerReviewStatus | Reviewed | en |
uws.scholarLevel | Faculty | en |