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Point Cloud Reconstruction From Truncated Geometry-Based Streams

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posted on 21.09.2021, 19:24 by DIOGO GARCIADIOGO GARCIA, Andre Souto, Gustavo Sandri, Tomas Borges, Ricardo Queiroz
Geometry-based point cloud compression (G-PCC) has been rapidly evolving in the context of international standards. Despite the inherent scalability of octree-based geometry description, current G-PCC attribute compression techniques prevent full scalability for compressed point clouds. In this paper, we present a solution to add scalability to attributes compressed using the region-adaptive hierarchical transform (RAHT), enabling the reconstruction of the point cloud using only a portion of the original bitstream. Without the full geometry information, one cannot compute the weights in which the RAHT relies on to calculate its coefficients for further levels of detail. In order to overcome this problem, we propose a linear relationship approximation relating the downsampled point cloud to the truncated inverse RAHT coefficients at that same level. The linear relationship parameters are sent as side information. After truncating the bitstream at a point corresponding to a given octree level, we can, then, recreate the attributes at that level. Tests were carried out and results attest the good approximation quality of the proposed technique.

Funding

Supported by CNPq under grants 88887.600000/2021-00 and 301647/2018-6.

History

Email Address of Submitting Author

srcaetano@gmail.com

ORCID of Submitting Author

0000-0002-3816-0873

Submitting Author's Institution

University of Brasilia

Submitting Author's Country

Brazil

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