Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attribute Compression of 3D Point Clouds Using Laplacian Sparsity
  Optimized Graph Transform   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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The categories of this paper are: q-fin
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: gr-qc
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: supr-con
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org