Fast vector quantization for efficient rendering of compressed point-clouds

Schnabel, Ruwen; Möser, Sebastian; Klein, Reinhard

doi:10.1016/j.cag.2008.01.014

Cited by 17 publications

(11 citation statements)

References 28 publications

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“…This method is able to handle large point clouds with higher quality than competing methods [KV05, SMK08, HMHB08] (which are not dictionary‐based in the sense of this survey). For a fixed compression rate between 0 and 2 bits per point, the Peak Signal to Noise Ratio (PSNR) of the approach of Digne et al [DCV14] is greater than these methods by roughly 9dB, 8dB and 2dB respectively.…”

Section: Compressionmentioning

confidence: 99%

A Survey on Data‐driven Dictionary‐based Methods for 3D Modeling

Lescoat

Ovsjanikov

Memari

et al. 2018

Computer Graphics Forum

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Dictionaries are very useful objects for data analysis, as they enable a compact representation of large sets of objects through the combination of atoms. Dictionary‐based techniques have also particularly benefited from the recent advances in machine learning, which has allowed for data‐driven algorithms to take advantage of the redundancy in the input dataset and discover relations between objects without human supervision or hard‐coded rules. Despite the success of dictionary‐based techniques on a wide range of tasks in geometric modeling and geometry processing, the literature is missing a principled state‐of‐the‐art of the current knowledge in this field. To fill this gap, we provide in this survey an overview of data‐driven dictionary‐based methods in geometric modeling. We structure our discussion by application domain: surface reconstruction, compression, and synthesis. Contrary to previous surveys, we place special emphasis on dictionary‐based methods suitable for 3D data synthesis, with applications in geometric modeling and design. Our ultimate goal is to enlight the fact that these techniques can be used to combine the data‐driven paradigm with design intent to synthesize new plausible objects with minimal human intervention. This is the main motivation to restrict the scope of the present survey to techniques handling point clouds and meshes, making use of dictionaries whose definition depends on the input data, and enabling shape reconstruction or synthesis through the combination of atoms.

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Section: Compressionmentioning

confidence: 99%

A Survey on Data‐driven Dictionary‐based Methods for 3D Modeling

Lescoat

Ovsjanikov

Memari

et al. 2018

Computer Graphics Forum

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“…Universal entropybased methods like Ziv and Lempel (1978) that perform well on data like text are less suited for point cloud compression as they cannot detect and exploit the hidden underlying geometric structures in the point cloud. Therefore a large branch of research is directed at shape-proxy-based compression methods, for example as outlined in Digne et al (2014); Golla et al (2014);Schnabel et al (2008). In such approaches, the key to an efficient compression is to find shape proxies that can be related to a large number of measured points.…”

Section: Point Cloud Visualization and Compressionmentioning

confidence: 99%

“…Therefore, the rationale of harnessing the IFC decomposition structure and building element surfaces is outlined in this section, but for these reasons the results of the conducted experiments are not compared to general purpose state of the art point cloud visualization and compression techniques. Similarly, using the IFC building model surfaces as input for existing shape-proxy-based compression methods (Digne et al, 2014;Golla et al, 2014;Schnabel et al, 2008) and compare their resulting compression ratios to surfaces fitted through the data, is something not investigated in this paper.…”

Section: Point Cloud Visualization and Compressionmentioning

confidence: 99%

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An IFC schema extension and binary serialization format to efficiently integrate point cloud data into building models

Krijnen

Beetz

2017

Advanced Engineering Informatics

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In this paper we suggest an extension to the Industry Foundation Classes (IFC) model to integrate point cloud datasets. The proposal includes a schema extension to the core model allowing the storage of points, either as Cartesian coordinates, points in parametric space of associated building element surfaces or as discrete height fields projected as grids onto building elements. To handle the considerable amounts of data generated in the process of scanning building structures, we present intelligent compression approaches combined with the Hierarchical Data Format (HDF) as an efficient serialization and an alternative to clear text encoded ISO 10303 part 21 files. Based on prototypical implementations we show results of various serialization options and their impacts on storage efficiency.In this proposal the deepened semantic relationships have been favoured over compression ratios. Nevertheless, with various near-lossless layers of compression and binary serialization applied, a compression ratio of up to 67.7% is obtained for a building model with integrated point clouds, compared to the raw source data. The binary serialization is able to handle hundreds of millions of points, out of which specific spatial and semantic subsets can rapidly be extracted due to the containerized hierarchical storage model and association to building elements. The authors advocate the use of binary storage for sizeable point cloud scans, but also show how especially the grid discretization can result into usable points cloud segments embedded into text-based IFC models.

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“…Its implementation on a GPU was used for ultraspectral sounder data by Wei and Huang [27]. Further, the vector quantization on a GPU was used for neural networks [28] and for computing images from point clouds [29], used successively also for volumetric compression [30]. GPU parallelization has also been applied to accelerate the visual categorization of image feature vectors [31].…”

Section: Related Workmentioning

confidence: 99%