HLOD Refinement Driven by Hardware Occlusion Queries

Information

• Publication Type: PhD-Thesis
• Workgroup(s)/Project(s): not specified
• Date: December 2008
• Date (End): 2008
• Second Supervisor: Eduardo Romero
• Rigorosum: 20. February 2008
• First Supervisor: Michael Wimmer

Abstract

In order to achieve interactive rendering of complex models comprising several millions of polygons, the amount of processed data has to be substantially reduced. Level-ofdetail (LOD) methods allow the amount of data sent to the GPU to be aggressively reduced at the expense of sacrificing image quality. Hierarchical level-of-detail (HLOD) methods have proved particularly capable of interactive visualisation of huge data sets by precomputing levels-of-detail at different levels of a spatial hierarchy. HLODs support out-of-core algorithms in a straightforward way and allow an optimal balance between CPU and GPU load during rendering. Occlusion culling represents an orthogonal approach for reducing the amount of rendered primitives. Occlusion culling methods aim to quickly cull the invisible part of the model and render only its visible part. Most recent methods use hardware occlusion queries (HOQs) to achieve this task. The effects of HLODs and occlusion culling can be successfully combined. Firstly, nodes which are completely invisible can be culled. Secondly, HOQ results can be used for visible nodes when refining an HLOD model; according to the degree of visibility of a node and the visual masking perceptual phenomenon, then it could be determined that there would be no gain in the final appearance of the image obtained if the node were further refined. In the latter case, HOQs allow more aggressive culling of the HLOD hierarchy, further reducing the amount of rendered primitives. However, due to the latency between issuing an HOQ and the availability of its result, the direct use of HOQs for refinement criteria cause CPU stalls and GPU starvation. This thesis introduces a novel error metric, taking visibility information (gathered from HOQs) as an integral part of refining an HLOD model, this being the first approach within this context to the best of our knowledge. A novel traversal algorithm for HLOD refinement is also presented for taking full advantage of the introduced HOQ-based error metric. The algorithm minimises CPU stalls and GPU starvation by predicting HLOD refinement conditions using spatio-temporal coherence of visibility. Some properties of the combined approach presented here involve improved performance having the same visual quality (whilst our occlusion culling technique still remained conservative). Our error metric supports both polygon-based and point-based HLODs, ensuring full use of HOQ results (our error metrics take full advantage of the information gathered in HOQs). Our traversal algorithm makes full use of the spatial and temporal coherency inherent in hierarchical representations. Our approach can be straightforwardly implemented.

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BibTeX

@phdthesis{charalambos-thesis_hlod,
  title =      "HLOD Refinement Driven by Hardware Occlusion Queries",
  author =     "Jean Pierre Charalambos",
  year =       "2008",
  abstract =   "In order to achieve interactive rendering of complex models
               comprising several millions of polygons, the amount of
               processed data has to be substantially reduced.
               Level-ofdetail (LOD) methods allow the amount of data sent
               to the GPU to be aggressively reduced at the expense of
               sacrificing image quality. Hierarchical level-of-detail
               (HLOD) methods have proved particularly capable of
               interactive visualisation of huge data sets by precomputing
               levels-of-detail at different levels of a spatial hierarchy.
               HLODs support out-of-core algorithms in a straightforward
               way and allow an optimal balance between CPU and GPU load
               during rendering. Occlusion culling represents an orthogonal
               approach for reducing the amount of rendered primitives.
               Occlusion culling methods aim to quickly cull the invisible
               part of the model and render only its visible part. Most
               recent methods use hardware occlusion queries (HOQs) to
               achieve this task. The effects of HLODs and occlusion
               culling can be successfully combined. Firstly, nodes which
               are completely invisible can be culled. Secondly, HOQ
               results can be used for visible nodes when refining an HLOD
               model; according to the degree of visibility of a node and
               the visual masking perceptual phenomenon, then it could be
               determined that there would be no gain in the final
               appearance of the image obtained if the node were further
               refined. In the latter case, HOQs allow more aggressive
               culling of the HLOD hierarchy, further reducing the amount
               of rendered primitives. However, due to the latency between
               issuing an HOQ and the availability of its result, the
               direct use of HOQs for refinement criteria cause CPU stalls
               and GPU starvation. This thesis introduces a novel error
               metric, taking visibility information (gathered from HOQs)
               as an integral part of refining an HLOD model, this being
               the first approach within this context to the best of our
               knowledge. A novel traversal algorithm for HLOD refinement
               is also presented for taking full advantage of the
               introduced HOQ-based error metric. The algorithm minimises
               CPU stalls and GPU starvation by predicting HLOD refinement
               conditions using spatio-temporal coherence of visibility.
               Some properties of the combined approach presented here
               involve improved performance having the same visual quality
               (whilst our occlusion culling technique still remained
               conservative). Our error metric supports both polygon-based
               and point-based HLODs, ensuring full use of HOQ results (our
               error metrics take full advantage of the information
               gathered in HOQs). Our traversal algorithm makes full use of
               the spatial and temporal coherency inherent in hierarchical
               representations. Our approach can be straightforwardly
               implemented.",
  month =      dec,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2008/charalambos-thesis_hlod/",
}