{"ID":2872657,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.08685","arxiv_id":"2509.08685","title":"Deep Unrolling of Sparsity-Induced RDO for 3D Point Cloud Attribute Coding","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.","short_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 \\m...","url_abs":"https://arxiv.org/abs/2509.08685","url_pdf":"https://arxiv.org/pdf/2509.08685v1","authors":"[\"Tam Thuc Do\",\"Philip A. Chou\",\"Gene Cheung\"]","published":"2025-09-10T15:23:21Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.IT\",\"cs.LG\"]","methods":"[]","has_code":false}