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

• URL: http://arxiv.org/abs/2509.08685v1
• Date: Wed, 10 Sep 2025 15:23:21 GMT
• Title: Deep Unrolling of Sparsity-Induced RDO for 3D Point Cloud Attribute Coding
• Authors: Tam Thuc Do, Philip A. Chou, Gene Cheung,
• Abstract summary: We study the problem of lossy attribute compression in a multi-resolution B-spline projection framework.<n>A target continuous 3D attribute function is first projected onto a sequence of nested subspaces.<n>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.
• Score: 24.375903431917163
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Given encoded 3D point cloud geometry available at the decoder, we study the problem of lossy attribute compression in a multi-resolution B-spline projection framework. A target continuous 3D attribute function is first projected onto a sequence of nested subspaces $\mathcal{F}^{(p)}_{l_0} \subseteq \cdots \subseteq \mathcal{F}^{(p)}_{L}$, where $\mathcal{F}^{(p)}_{l}$ 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 by variable-complexity unrolling of a rate-distortion (RD) optimization algorithm into a feed-forward network, where the rate term is the sparsity-promoting $\ell_1$-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.

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