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On sparse voxel DAG...
On sparse voxel DAGs and memory efficient compression of surface attributes for real-time scenarios
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- Dolonius, Dan, 1985 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- ISBN 9789179056315
- Gothenburg, 2022
- Engelska.
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Abstract
Ämnesord
Stäng
- The general shape of a 3D object can expeditiously be represented as, e.g., triangles or voxels, while smaller-scale features usually are parameterized over the surface of the object. Such features include, but are not limited to, color details, small-scale surface-normal variations, or even view-dependent properties required for the light-surface interactions. This thesis is a collection of four papers that focus on new ways to compress and efficiently utilize surface data in 3D for real-time usage. In Paper IA and IB , we extend upon the concept of sparse voxel DAGs, a real-time compression format of a voxel-grid, to allow an attribute mapping with a negligible impact on the size. The main contribution, however, is a novel real-time compression format of the mapped colors over such sparse voxel surfaces. Paper II aims to utilize the results of the previous papers to achieve uv-free texturing of surfaces, such as triangle meshes, with optimized run-time minification as well as magnification filtering. Paper III extends upon previous compact representations of view dependent radiance using spherical gaussians (SG). By using a convolutional neural network, we are able to compress the light-field by finding SGs with free directions, amplitudes and sharpnesses, whereas previous methods were limited to only free amplitudes in similar scenarios.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- directed acyclic graph
- surface properties
- neural networks
- voxel
- compression
- spherical gaussians
- light field
- octree
- filtering
- geometry
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- dok (ämneskategori)
- vet (ämneskategori)
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