Octree-based Point-Cloud Compression

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dc.contributor.authorSchnabel, Ruwenen_US
dc.contributor.authorKlein, Reinharden_US
dc.contributor.editorMario Botsch and Baoquan Chen and Mark Pauly and Matthias Zwickeren_US
dc.date.accessioned2014-01-29T16:38:15Z
dc.date.available2014-01-29T16:38:15Z
dc.date.issued2006en_US
dc.description.abstractIn this paper we present a progressive compression method for point sampled models that is specifically apt at dealing with densely sampled surface geometry. The compression is lossless and therefore is also suitable for storing the unfiltered, raw scan data. Our method is based on an octree decomposition of space. The point-cloud is encoded in terms of occupied octree-cells. To compress the octree we employ novel prediction techniques that were specifically designed for point sampled geometry and are based on local surface approximations to achieve high compression rates that outperform previous progressive coders for point-sampled geometry. Moreover we demonstrate that additional point attributes, such as color, which are of great importance for point-sampled geometry, can be well integrated and efficiently encoded in this framework.en_US
dc.description.seriesinformationSymposium on Point-Based Graphicsen_US
dc.identifier.isbn3-905673-32-0en_US
dc.identifier.issn1811-7813en_US
dc.identifier.urihttps://doi.org/10.2312/SPBG/SPBG06/111-120en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): E.4 [Data]: Coding and information theory - Data compaction and compression; I.3.5 [Computer Graphics]: Computational geometry and object modeling - Curve, surface, solid and object representationsen_US
dc.titleOctree-based Point-Cloud Compressionen_US
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