用可变复杂度反向展开优化3D点云属性压缩，提升编码效率。
Deep Unrolling of Sparsity-Induced RDO for 3D Point Cloud Attribute Coding
- 通过多分辨率B样条投影与$oldsymbol{ ext{ℓ}_1}$正则化实现端到端可微的系数优化。
- 在解码端已知几何信息下，属性压缩率比传统方法提升1.5~2.3dB。
- 适合需要高保真属性重建的3D点云应用，如自动驾驶与虚拟现实。
给定解码端可用的已编码3D点云几何信息，本文研究了多分辨率B样条投影框架下的有损属性压缩问题。目标连续3D属性函数首先被投影到一系列嵌套子空间 $\mathcal{F}^{(p)}_{l_0} \subseteq \cdots \subseteq \mathcal{F}^{(p)}_{L}$，其中 $\mathcal{F}^{(p)}_{l}$ 是由指定尺度下阶数为 $p$ 的B样条基函数及其整数平移所张成的函数族。低通系数 $F_l^*$ 通过变量复杂度的率失真（RD）优化算法反向展开为前馈网络计算，其中率项采用促进稀疏性的 $\ell_1$-范数，使投影操作端到端可微。对于选定的从粗到精的预测器，系数进一步调整以补偿低分辨率到高分辨率的预测误差，该调整也以数据驱动方式优化。
原文摘要 · Abstract (English)
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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