Towards Efficient Online Compression of Incrementally Acquired Point Clouds

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dc.contributor.authorGolla, Timen_US
dc.contributor.authorSchwartz, Christopheren_US
dc.contributor.authorKlein, Reinharden_US
dc.contributor.editorJan Bender and Arjan Kuijper and Tatiana von Landesberger and Holger Theisel and Philipp Urbanen_US
dc.date.accessioned2014-12-16T07:25:56Z
dc.date.available2014-12-16T07:25:56Z
dc.date.issued2014en_US
dc.description.abstractWe present a framework for the online compression of incrementally acquired point cloud data. For this, we extend an existing vector quantization-based offline point cloud compression algorithm to handle the challenges that arise from the envisioned online scenario. In particular, we learn a codebook in advance from training data and replace a computationally demanding part of the algorithm with a faster alternative. We show that the compression ratios and reconstruction quality are comparable to the offline version while the speed is sufficiently improved. Furthermore, we investigate how well codebooks that are generated from different amounts of training data generalize to larger sets of point cloud data.en_US
dc.description.seriesinformationVision, Modeling & Visualizationen_US
dc.identifier.isbn978-3-905674-74-3en_US
dc.identifier.urihttps://doi.org/10.2312/vmv.20141271en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectE.4 [Coding and Information Theory]en_US
dc.subjectData compaction and compressionen_US
dc.subjectI.3.3 [Computer Graphics]en_US
dc.subjectPicture/Image Generationen_US
dc.subjectDigitizing and scanningen_US
dc.subjectI.4.1 [Image processing and computer vision]en_US
dc.subjectDigitizing and Image Captureen_US
dc.subjectQuantizationen_US
dc.titleTowards Efficient Online Compression of Incrementally Acquired Point Cloudsen_US
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