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Moxel DAGs: Connecting Material Information to High Resolution Sparse Voxel DAGs

As time goes on, the demand for higher resolution and more visually rich images only increases. Unfortunately, creating these more realistic computer graphics is pushing our computational resources to their limits.
In realistic rendering, one of the common ways 3D objects are represented is as volumetric elements called voxels. Traditionally, voxel data structures are known for their high memory requirements. One of the standard ways these requirements are minimized is by storing the voxels in a sparse voxel octree (SVO). Very recently, a method called High Resolution Sparse Voxel DAGs was presented that can store binary voxel data orders of magnitudes more efficiently than SVOs. This memory efficiency is achieved by converting the tree into a directed acyclic graph (DAG). The method was also shown to have competitive rendering performance to recent GPU ray tracers. Unfortunately, it does not support storing collections of rendering attributes, commonly called materials. These represent a given object's reflectance properties, and are necessary for calculating its perceived color.
We present a method for connecting material information to High Resolution Sparse Voxel DAGs for mid-level scenes, with multiple meshes, and several different materials. This is achieved using an extended Sparse Voxel DAG, called a Moxel DAG, and an external data structure for holding the material information, we call a Moxel Table. Our method is much more memory efficient than traditional SVOs, and only increases in efficiency in comparison when at higher resolutions. Because it stores the equivalent information as SVOs, it achieves the exact same visual quality at the same resolutions.

Identiferoai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-2567
Date01 June 2015
CreatorsWilliams, Brent Robert
PublisherDigitalCommons@CalPoly
Source SetsCalifornia Polytechnic State University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceMaster's Theses

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