Geometry and Attribute Compression for Voxel Scenes

Voxel‐based approaches are today's standard to encode volume data. Recently, directed acyclic graphs (DAGs) were successfully used for compressing sparse voxel scenes as well, but they are restricted to a single bit of (geometry) information per voxel. We present a method to compress arbitrary...

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Vydáno v:Computer graphics forum Ročník 35; číslo 2; s. 397 - 407
Hlavní autoři: Dado, Bas, Kol, Timothy R., Bauszat, Pablo, Thiery, Jean-Marc, Eisemann, Elmar
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Blackwell Publishing Ltd 01.05.2016
Témata:
Analysis
Categories and Subject Descriptors (according to ACM CCS)
Central processing units
Compressing
CPUs
Data compression
Decoupling
Geometry
Graph theory
Hardness
Hardware
I.3.3 [Computer Graphics]: Picture/Image Generation-Display algorithms
I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism-Raytracing
Image processing systems
Reflectivity
State of the art
Studies
Video compression
ISSN:0167-7055, 1467-8659
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Popis
Shrnutí:Voxel‐based approaches are today's standard to encode volume data. Recently, directed acyclic graphs (DAGs) were successfully used for compressing sparse voxel scenes as well, but they are restricted to a single bit of (geometry) information per voxel. We present a method to compress arbitrary data, such as colors, normals, or reflectance information. By decoupling geometry and voxel data via a novel mapping scheme, we are able to apply the DAG principle to encode the topology, while using a palette‐based compression for the voxel attributes, leading to a drastic memory reduction. Our method outperforms existing state‐of‐the‐art techniques and is well‐suited for GPU architectures. We achieve real‐time performance on commodity hardware for colored scenes with up to 17 hierarchical levels (a 128K3voxel resolution), which are stored fully in core.
Bibliografie:istex:4289E5F748A6988744062EF2A54DE6F5F564096D
ArticleID:CGF12841
Supporting Information
ark:/67375/WNG-VXD71Q9S-V
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12841