Self-Similarity-Based Compression of Point Clouds, with Application to Ray Tracing

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dc.contributor.authorHubo, Eriken_US
dc.contributor.authorMertens, Tomen_US
dc.contributor.authorHaber, Tomen_US
dc.contributor.authorBekaert, Philippeen_US
dc.contributor.editorM. Botsch and R. Pajarola and B. Chen and M. Zwickeren_US
dc.date.accessioned2014-01-29T16:52:13Z
dc.date.available2014-01-29T16:52:13Z
dc.date.issued2007en_US
dc.description.abstractMany real-world, scanned surfaces contain repetitive structures, like bumps, ridges, creases, and so on.We present a compression technique that exploits self-similarity within a point-sampled surface. Our method replaces similar surface patches with an instance of a representative patch. We use a concise shape descriptor to identify and cluster similar patches. Decoding is achieved through simple instancing of the representative patches. Encoding is efficient, and can be applied to large datasets consisting of millions of points. Moreover, our technique offers random access to the compressed data, making it applicable to ray tracing, and easily allows for storing additional point attributes, like normals.en_US
dc.description.seriesinformationEurographics Symposium on Point-Based Graphicsen_US
dc.identifier.isbn978-3-905673-51-7en_US
dc.identifier.issn1811-7813en_US
dc.identifier.urihttps://doi.org/10.2312/SPBG/SPBG07/129-137en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational geometry and object modeling - Curve, surface, solid and object representations; E.4 [Data]: Coding and information theory - Data compaction and compressionen_US
dc.titleSelf-Similarity-Based Compression of Point Clouds, with Application to Ray Tracingen_US
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