Dynamic meshing using adaptively sampled distance fields
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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

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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


Dynamic meshing using adaptively sampled distance fields

Demo!


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