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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

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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
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
static level of detail meshes
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

dynamic level of detail meshes
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
adaptively sampled distance fields
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
adaptively sampled distance fields1
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

triangulating adfs
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
dynamic meshing using adfs
Dynamic Meshing Using ADFs

Initial Mesh

Viewing

Parameters

Dynamic

Mesh

Mesh

Modifications

initial mesh creation
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
initial mesh creation1
Initial Mesh Creation
  • View-independent mesh creation
  • Assign triangle count to cell
  • Calculate child weights
  • Distribute trianglecount between children

300

initial mesh creation2
Initial Mesh Creation
  • View-independent mesh creation
  • Assign triangle count to cell
  • Calculate child weights
  • Distribute trianglecount betweenchildren

300

2

1

3

initial mesh creation3
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

dynamic meshes
Dynamic Meshes
  • Updating the active list
  • List of cells generating triangles
  • Ascend tree to reduce detail
  • Descend treeto increasedetail
dynamic meshes1
Dynamic Meshes
  • Updating the active list
  • List of cells generating triangles
  • Ascend tree toreduce detail
  • Descend treeto increasedetail
dynamic meshes2
Dynamic Meshes
  • Updating the active list
  • List of cells generating triangles
  • Ascend tree toreduce detail
  • Descend treeto increasedetail
viewing parameters
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,...
conclusions
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
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