1 / 20

Gigantic Deformable Surfaces Hierarhical RLE Level Sets

Gigantic Deformable Surfaces Hierarhical RLE Level Sets. Ben Houston , Neuralsoft, Frantic Films Michael B. Nielsen , University of Aarhus Christopher Batty , Frantic Films Ola Nilsson , Link öping Institute of Technology Ken Museth , Link öping Institute of Technology.

vanna-kirkland
Download Presentation

Gigantic Deformable Surfaces Hierarhical RLE Level Sets

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Gigantic Deformable Surfaces Hierarhical RLE Level Sets Ben Houston, Neuralsoft, Frantic Films Michael B. Nielsen, University of Aarhus Christopher Batty, Frantic Films Ola Nilsson, Linköping Institute of Technology Ken Museth, Linköping Institute of Technology

  2. Part 1 - Data Structure Michael Bang Nielsen

  3. An Implicit Representation for Deformable Surfaces:Level Sets Surface deformation governed by PDE.

  4. Problem Statement • What? • 1) Memory efficient 2) Computationally efficient 3) ”Out-Of-The-Box” 4) Versatile 5) Compatible representation of high resolution deforming surfaces. • Why? • Deforming surfaces have wide applicability in computer graphics and simulation: Fluids, Geometric Modelling, Morphing... • How? • The Hierarchical-RLE (H-RLE) datastructure:Combine best features of the RLE Sparse Level Set [Houston,Batty,Wiebe 2004] and the DT-Grid [Nielsen and Museth 2004].

  5. 1D Run Length Encoding Run Length Encoding (RLE) partitions a sequence of data into runs, each associated with a specific runcode.

  6. 2D Hierarchical RLE RLE Segment Y RLE Block X RLE Block

  7. Toolbox of Algorithms The algorithms are recursive in the dimension of the H-RLE. • Construction time is optimal: O(D). • Rebuilding the narrow band: O(D). • Sequential and stencil access times are optimal: O(1) per grid point. • Axis aligned CSG operations are optimal: O(D1+D2). • Random and neighbor access times are logarithmic in the number of runs in at most k RLE segments, where k is the dimension.

  8. Features of H-RLE • Data structure and algorithms are fast in practice: Accomodate the cache hierarchies of modern computers. • Asymptotically optimal memory footprint, O(D), and near-optimal memory footprint in practice. • Level set simulations can go ”out-of-the-box” • Generalizes to N dimensions.

  9. Part 2 - Applications Ben Houston

  10. Unified Implicit Object Representation • The “Augmented Level Set” • Combined level set with any number of auxiliary subordinate fields. • UVW texture coords, alpha channels, interface velocities… • Abstraction layer for operations that modify both the level set and subordination fields: • mesh to level set, boolean operations, fluid advection, level set rendering, etc • H-RLE level set is suited since H-RLE grid can be shared between multiple fields.

  11. Efficient Clipping • Since we classify non-narrow band space we can use store “unclosed level sets.”

  12. Fast Scan Conversion Fast Scan Conversion of Large Meshes • Input mesh must be closed and non-self intersecting. • MAUCH, S. 2000. A fast algorithm for computing the closest point and distance transform. Happy Buddha 600x1445x600 = 520 million voxels6 voxel narrow band59 sec on 1.5GHz Pentium Mobile.

  13. Robust Scan Conversion Scan Conversion of Open & Intersecting Meshes • We thus take a visibility-testing approach via the use of ray tracing. • Conceptually, for each point we cast a number of rays to sample the world around and if the majority of the rays hit back faces, that location is interior, otherwise it is exterior.

  14. Robust Scan Conversion • This robust method can be applied to H-RLE level set scan conversion such that less than O (n^3) space or time is required. • Robustness has been production tested.

  15. Direct Ray Tracing • Computational equivalent to ray tracing large non-compressed narrow band level sets because of the cache coherent nature of the H-RLE level set.

  16. Surface Deformation

  17. Fluid Simulation • It is easy to encoding whole fluid volumes, as opposed to just the interface, using the H-RLE grid structure. Fluid Augmented Level Set • Interface Level Set, Volume Staggered Velocities. Occlusions Augmented Level Set: • Interface Level Set, Centered SurfaceVelocities, Surface Slip Conditions Computation and storage scales in terms fluid volume.

  18. Fluid Simulation • We encountered the mass conversation pressure projection as the main storage bottleneck. • Our solution was to apply RLE compression to both the matrix and vector representations. • Compress the 4 unique diagonals using 4 RLE streams. • Requires at most O( v ) storage and computation for matrix and vector representations. • Matrix and vector operations used in the conjugate gradient algorithm all require near sequential access thus computation scales in terms of storage.

  19. Performance Evaluation • Model Storage Comparison • Level Set Propagation Comparison • Curvature-based flow (i.e. Heat equation)

  20. Thank You! More information online: http://www.exocortex.org/hrle2005/ “Hierarchical RLE Level Sets” (in review) Conditionally Accepted to ACM TOG. Provides all details of the data structure and uses described today. “The Visual Simulation of Wispy Smoke”, Today @ 1:30 in room West Hall B.

More Related