1 / 18

Dynamic Meshing Using Adaptively Sampled Distance Fields

Dynamic Meshing Using Adaptively Sampled Distance Fields. Jackson Pope, Sarah F. Frisken and Ronald N. Perry MERL – Mitsubishi Electric Research Laboratory. Level of Detail Meshing. Models often feature more detail than required laser range scanners generate over-triangulated meshes

obelia
Télécharger la présentation

Dynamic Meshing Using Adaptively Sampled Distance Fields

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. Dynamic Meshing Using Adaptively Sampled Distance Fields Jackson Pope, Sarah F. Frisken and Ronald N. Perry MERL – Mitsubishi Electric Research Laboratory

  2. Level of Detail Meshing • Models often feature more detail than required • laser range scanners generate over-triangulated meshes • distant objects can be less detailed with no loss of visual quality • real-time applications have restricted frame time available for rendering objects • Render objects differently according to viewing parameters  Level of detail meshing: • pre-generate one or more static meshes • adaptively refine and decimate a dynamic mesh

  3. Static Level of Detail Meshes • Use different models according to visual importance • simple, usually distance based • visual artifacts when switching models • restrictive Five static LOD models of the same object with 202264, 45544, 3672, 232 and 28 triangles respectively

  4. Dynamic Level of Detail Meshes • Single mesh created, elements added and removed according to viewing parameters • optimal mesh for every frame • requires per frame calculation • Examples: View Dependent Progressive Mesh (Hoppe 97), Hierarchical Dynamic Simplification (Luebke and Erikson 97) • fast, but limited by input geometry • hard to integrate with other requirements, e.g. collision detection

  5. Adaptively Sampled Distance Fields • Distance fields represent the distance to the object surface • can be signed to denote inside/outside • can be minimal distance • gradient of the field corresponds to surface normal (at the surface) or direction to the surface • Adaptively Sampled Distance Fields (ADFs) • sample the distance in a spatial domain • hierarchical, to enable adaptive, detail-directed sampling • the dynamic meshing implementation is octree based

  6. Adaptively Sampled Distance Fields • Distance values stored in octree cell corners • three types of cells: inside, outside and surface • all distance values for inside cells are inside • all distance values for outside cells are outside • surface cells contain both inside and outside values An object and its 2D ADF showing adaptive cell subdivision

  7. Triangulating ADFs • Generate a mesh vertex in each surface cell • different to edge based techniques (e.g. Marching Cubes) • Connect vertices to those in neighbouring cells to form triangles • Mesh vertices are relaxed onto the surface by following the distance field to the surface • Algorithm details available in: • Frisken and Perry, “Kizamu: A System for Sculpting Digital Characters”, SIGGRAPH 2001

  8. Dynamic Meshing Using ADFs Initial Mesh Viewing Parameters Dynamic Mesh Mesh Modifications

  9. Initial Mesh Creation • Data Preparation in surface cells • store a normal cone in each surface cell to enable fast back-face culling and silhouette detection • normal cone in a cell encompasses those of its children • create a neighbour look up table • position a mesh vertex on the surface in each cell

  10. Initial Mesh Creation • View-independent mesh creation • Assign triangle count to cell • Calculate child weights • Distribute trianglecount between children 300

  11. Initial Mesh Creation • View-independent mesh creation • Assign triangle count to cell • Calculate child weights • Distribute trianglecount betweenchildren 300 2 1 3

  12. Initial Mesh Creation • View-independent mesh creation • Assign triangle count to cell • Calculate child weights • Distribute trianglecount betweenchildren 300 150 100 50 2 1 3

  13. Dynamic Meshes • Updating the active list • List of cells generating triangles • Ascend tree to reduce detail • Descend treeto increasedetail

  14. Dynamic Meshes • Updating the active list • List of cells generating triangles • Ascend tree toreduce detail • Descend treeto increasedetail

  15. Dynamic Meshes • Updating the active list • List of cells generating triangles • Ascend tree toreduce detail • Descend treeto increasedetail

  16. Viewing Parameters • Cells are weighted according to viewing parameters and weighting functions • We currently use: • on silhouette, back-facing, in view frustum, surface roughness • Can also use: • distance to viewpoint, screen-space projected error, specular highlight,...

  17. Conclusions • ADFs can be used to generate dynamic triangle meshes • triangle meshes generated from ADFs are detail-directed, low triangle counts remain in areas of geometrical simplicity • octree structure provides framework for fast view frustum and back face culling • resulting dynamic mesh has low triangle counts in smooth and visually unimportant regions • In addition, ADFs provide useful information for collision detection and real-time physics

  18. Demo!

More Related