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An Efficient Progressive Refinement Strategy for Hierarchical Radiosity

An Efficient Progressive Refinement Strategy for Hierarchical Radiosity. Nicolas Holzschuch, François Sillion and George Drettakis. iMAGIS/IMAG, Grenoble — France. A joint research project of IMAG and INRIA. Motivation. Hierarchical radiosity is a significant step in radiosity algorithms

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An Efficient Progressive Refinement Strategy for Hierarchical Radiosity

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  1. An Efficient Progressive Refinement Strategy for Hierarchical Radiosity Nicolas Holzschuch, François Sillion and George Drettakis iMAGIS/IMAG, Grenoble — France A joint research project of IMAG and INRIA

  2. Motivation • Hierarchical radiosity is a significant step in radiosity algorithms • creates links between patches and refines them • linear in the number of elements created • Proceeds top-down: • First establish links between input surfaces • Then refine these links where needed

  3. Motivation (2) • “Initial linking” step quadratic in the number of polygons • Many top-level links will never carry significant energy • Subdivision is often too high

  4. Proposed improvements • Delaying initial linking of input surfaces • Reducing the number of links

  5. Our test scenes

  6. Initial Linking • Proportion of top-level links with BF < 

  7. Subdivision

  8. Previous work • Hierarchical radiosity: (Hanrahan, 90- 91) • Link refinement based on radiance and form-factor • proceed from top to bottom • multigridding • Importance-driven hierarchical radiosity: (Smits, Arvo & Salesin, 92) • Links refined using importance and influence on the final image

  9. Previous work (2) • Hierarchical radiosity and discontinuity meshing: (Lischinski, Tampieri & Greenberg 93) • First refine patches using a discontinuity mesh, then re-refine using radiosity and form-factor • Structured sampling: (Drettakis & Fiume 93) • Adapt mesh to illumination structure

  10. Delaying initial linking • Delay top-level linking between input surfaces until strictly necessary • First iteration results achieved more rapidly • Spread computation over several iterations • Avoid part of initial linking computation; gain on total computation time

  11. Classification of pairs • Initially, all pairs of polygons are un-classified Un-classified • “Important” pairs progressively become classified. • We compute visibility tests only for classified pairs. Classified Un-classified Visible Partial Invisible

  12. Linking algorithm • First record all polygon pairs as un-classified. • As soon as a pair qualifies for linking (B*F > ), compute visibility and link it accordingly. • The remainder of the algorithm is not modified.

  13. Energy Balance • Partially linked polygons do not emit all their energy • Un-radiated energy affects the energy balance • quantify the importance of this lost energy • compare it with the overall precision of the algorithm. • Unit sum of form-factors allows estimation of lost energy

  14. Energy Balance

  15. Reducing the number of links • Perform an a posteriori test to determine whether refinement was required:

  16. Reducing the number of links • We cancel the refinement if the following four expressions are true:

  17. Results: first iteration

  18. Results: ten iterations

  19. Subdivision

  20. Conclusion • Delaying top-level linking between input surfaces • storage costs are reduced • we obtain first results earlier • still quadratic in the number of input surfaces • Reducing the number of links • improved subdivision criterion • limits un-necessary subdivisions • Future work: • Simplify already subdivided meshes

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