Entropy-driven Progressive Compression of 3D Point Clouds

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dc.contributor.authorZampieri, Armanden_US
dc.contributor.authorDelarue, Guillaumeen_US
dc.contributor.authorBakr, Nachwa Abouen_US
dc.contributor.authorAlliez, Pierreen_US
dc.contributor.editorHu, Ruizhenen_US
dc.contributor.editorLefebvre, Sylvainen_US
dc.date.accessioned2024-06-20T07:54:50Z
dc.date.available2024-06-20T07:54:50Z
dc.date.issued2024
dc.description.abstract3D point clouds stand as one of the prevalent representations for 3D data, offering the advantage of closely aligning with sensing technologies and providing an unbiased representation of a measured physical scene. Progressive compression is required for real-world applications operating on networked infrastructures with restricted or variable bandwidth. We contribute a novel approach that leverages a recursive binary space partition, where the partitioning planes are not necessarily axis-aligned and optimized via an entropy criterion. The planes are encoded via a novel adaptive quantization method combined with prediction. The input 3D point cloud is encoded as an interlaced stream of partitioning planes and number of points in the cells of the partition. Compared to previous work, the added value is an improved rate-distortion performance, especially for very low bitrates. The latter are critical for interactive navigation of large 3D point clouds on heterogeneous networked infrastructures.en_US
dc.description.number5
dc.description.sectionheadersSimplify and Compress
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15130
dc.identifier.issn1467-8659
dc.identifier.pages16 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15130
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15130
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Mathematics of computing->Coding theory; Applied computing->Computer-aided design; Theory of computation->Data compression; Computing methodologies->Point-based models
dc.subjectMathematics of computing
dc.subjectCoding theory
dc.subjectApplied computing
dc.subjectComputer
dc.subjectaided design
dc.subjectTheory of computation
dc.subjectData compression
dc.subjectComputing methodologies
dc.subjectPoint
dc.subjectbased models
dc.titleEntropy-driven Progressive Compression of 3D Point Cloudsen_US
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