Attribute Compression of 3D Point Clouds Using Laplacian Sparsity Optimized Graph Transform

Ge Li; Kui Fan; Yiting Shao; Zhaobin Zhang; Zhu Li

Attribute Compression of 3D Point Clouds Using Laplacian Sparsity Optimized Graph Transform

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arxiv 1710.03532 v1 pith:FOYJ3LM2 submitted 2017-10-10 cs.MM

Yiting Shao , Zhaobin Zhang , Zhu Li , Kui Fan , Ge Li This is my paper · ORCID

classification cs.MM

keywords pointcloudcompressioncontentgraphoptimizedtransformefficiency

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3D sensing and content capture have made significant progress in recent years and the MPEG standardization organization is launching a new project on immersive media with point cloud compression (PCC) as one key corner stone. In this work, we introduce a new binary tree based point cloud content partition and explore the graph signal processing tools, especially the graph transform with optimized Laplacian sparsity, to achieve better energy compaction and compression efficiency. The resulting rate-distortion operating points are convex-hull optimized over the existing Lagrangian solutions. Simulation results with the latest high quality point cloud content captured from the MPEG PCC demonstrated the transform efficiency and rate-distortion (R-D) optimal potential of the proposed solutions.

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