Fast compression and access of LiDAR point clouds using wavelets

Wiman, Håkan; Qin, Yuchu

doi:10.1109/urs.2009.5137589

Cited by 9 publications

(6 citation statements)

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“…The difficulty here comes from the lack of topological information of the point cloud representation. In [Wiman and Qin 2009]25 ⃝, a wavelet-based framework is proposed for LIDAR-like point cloud compression, called WALZ (WAvelet Lidar Zipper). Each coordinate component, X , Y , and Z , is independently wavelet-transformed as a 1D vector.…”

Section: Direct Exploitation Of 3d Correlationsmentioning

confidence: 99%

3D Point Cloud Compression

Cao

Preda

Zaharia

2019

The 24th International Conference on 3D Web Technology

120

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In recent years, 3D point clouds have enjoyed a great popularity for representing both static and dynamic 3D objects. When compared to 3D meshes, they offer the advantage of providing a simpler, denser and more close-to-reality representation. However, point clouds always carry a huge amount of data. For a typical example of a point cloud with 0.7 million points per 3D frame at 30 fps, the point cloud raw video needs a bandwidth around 500MB/s. Thus, efficient compression methods are mandatory for ensuring the storage/transmission of such data, which include both geometry and attribute information. In the last years, the issue of 3D point cloud compression (3D-PCC) has emerged as a new field of research. In addition, an ISO/MPEG standardization process on 3D-PCC is currently ongoing. In this paper, a comprehensive overview of the 3D-PCC state-of-the-art methods is proposed. Different families of approaches are identified, described in details and summarized, including 1D traversal compression, 2D-oriented techniques, which take leverage of existing 2D image/video compression technologies and finally purely 3D approaches, based on a direct analysis of the 3D data. CCS CONCEPTS • Computing methodologies → Point-based models; • Information systems → Data compression; • General and reference → Surveys and overviews.

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Section: Direct Exploitation Of 3d Correlationsmentioning

confidence: 99%

3D Point Cloud Compression

Cao

Preda

Zaharia

2019

The 24th International Conference on 3D Web Technology

120

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“…A preliminary study of the only progressive compression method for LiDAR data was reported (Wiman and Qin 2009). This method uses a wavelet transform, which is performed independently on each of the x, y and z coordinates and is coded as 1D vectors.…”

Section: Related and Similar Workmentioning

confidence: 99%

Lossless progressive compression of LiDAR data using hierarchical grid level distribution

Lipuš

Žalik

2015

Remote Sensing Letters

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This letter considers a new approach for the lossless progressive compression of light detection and ranging (LiDAR) data stored within a LAS file (public file format for the interchange of three-dimensional point cloud data), which is used for storing the results of LiDAR scanning. The presented method builds a hierarchical data model for arranging LAS points into different levels in one pass. The higher levels are compressed using variable length and arithmetic coding, whilst the lower levels apply a prediction model of the non-progressive compression schema. The order of the points, as captured by the LiDAR scanner, has to be preserved within each level as better compression ratios are achieved in this way.

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“…(Kalbermatten, 2010, pp. 45-77), (Mocanu et al, 2011;Wünsche, 1998;Wiman and Yuchu, 2009). For example (Olanda et al, 2014;Bjørke and Nilsen, 2003;Bruun and Nilsen, 2003) give an overview of wavelet techniques for simplification of digital terrain models.…”

Section: Introductionmentioning

confidence: 99%

Context-sensitive Filtering of Terrain Data based on Multi Scale Analysis

Goebbels

Pohle-Fröhlich

2015

Proceedings of the 10th International Conference on Computer Graphics Theory and Applications

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This paper addresses triangle count reduction for regular terrain triangulations. Supported by additional information available from cadastre data, non-linear low pass filtering based on wavelet coefficients is applied. The aim is to generate a simplified view of a city model's terrain so that still landmarks can be easily recognized. Height data, that are not critical for visual impressions, are compressed with lower resolution than height data belonging to landmarks. Results are presented for the German city of Krefeld.

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Fast compression and access of LiDAR point clouds using wavelets

Cited by 9 publications

References 0 publications

3D Point Cloud Compression

3D Point Cloud Compression

Lossless progressive compression of LiDAR data using hierarchical grid level distribution

Context-sensitive Filtering of Terrain Data based on Multi Scale Analysis

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