Deep Unrolling of Sparsity-Induced RDO for 3D Point Cloud Attribute Coding.

Authors

Tam Thuc Do, Philip A Chou, Gene Cheung

Published in

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. Volume PP. Aug 11, 2026. Epub Aug 11, 2026.

Abstract

We study the problem of lossy attribute compression, given encoded 3D point cloud geometry available at the decoder, in a multi-resolution B-spline projection framework. A target continuous 3D attribute function is first projected onto a sequence of nested subspaces F^(p) _l0 ⊆ · · · ⊆ F^(p) _L , where F^(p) _l is a family of functions spanned by a B-spline basis function of order p at a chosen scale and its integer shifts. The projected low-pass coefficients F^∗ _l are computed via variable-complexity unrolling of a rate-distortion (RD) optimization algorithm into a feed-forward network, where the rate term is the sparsity-promoting ℓ₁-norm. Thus, the projection operation is end-to-end differentiable. For a chosen coarse-to-fine predictor, the coefficients are then adjusted to account for the prediction from a lower-resolution to a higher-resolution, which is also optimized in a data-driven manner.

PMID:
42579590
Bibliographic data and abstract were imported from PubMed on 12 Aug 2026.

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