Hierarchical RLE level set: A compact and versatile deformable surface representation

  • Authors:

  • Ben Houston;Michael B. Nielsen;Christopher Batty;Ola Nilsson;Ken Museth

  • Affiliations:

  • Exocortex Technologies, Frantic Films, Ottawa, Ont., Canada;University of Århus, Norrköping, Sweden;University of British Columbia, Frantic Films, Vancouver, BC, Canada;Linköping Institute of Technology, Norrköping, Sweden;Linköping Institute of Technology and University of Århus, Norrköping, Sweden

  • Venue:
  • ACM Transactions on Graphics (TOG)
  • Year:
  • 2006

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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.