Surface parameterization is a fundamental geometry processing problem with
rich downstream applications. Traditional approaches are designed to operate on
well-behaved mesh models with high-quality triangulations that are laboriously
produced by specialized 3D modelers, and thus unable to meet the processing
demand for the current explosion of ordinary 3D data. In this paper, we seek to
perform UV unwrapping on unstructured 3D point clouds. Technically, we propose
ParaPoint, an unsupervised neural learning pipeline for achieving global
free-boundary surface parameterization by building point-wise mappings between
given 3D points and 2D UV coordinates with adaptively deformed boundaries. We
ingeniously construct several geometrically meaningful sub-networks with
specific functionalities, and assemble them into a bi-directional cycle mapping
framework. We also design effective loss functions and auxiliary differential
geometric constraints for the optimization of the neural mapping process. To
the best of our knowledge, this work makes the first attempt to investigate
neural point cloud parameterization that pursues both global mappings and free
boundaries. Experiments demonstrate the effectiveness and inspiring potential
of our proposed learning paradigm. The code will be publicly available.

本文提出了 ParaPoint，一种无监督的神经学习管道，通过在不规则的三维点云上建立 3D 点与 2D UV 坐标之间的点对点映射，实现全局自由边界表面参数化。我们构建了几个具有特定功能的几何意义的子网络，并将它们组装成双向循环映射框架。通过设计有效的损失函数和辅助微分几何约束，优化了神经映射过程。实验证明了我们提出的学习范式的有效性和激发潜力。

ParaPoint: 学习 3D 点云的全局自由边界表面参数化

ParaPoint: Learning Global Free-Boundary Surface Parameterization of 3D  Point Clouds

We study 3D point cloud attribute compression using a volumetric approach:
given a target volumetric attribute function $f : \mathbb{R}^3 \rightarrow
\mathbb{R}$, we quantize and encode parameter vector $\theta$ that
characterizes $f$ at the encoder, for reconstruction
$f_{\hat{\theta}}(\mathbf{x})$ at known 3D points $\mathbf{x}$'s at the
decoder. Extending a previous work Region Adaptive Hierarchical Transform
(RAHT) that employs piecewise constant functions to span a nested sequence of
function spaces, we propose a feedforward linear network that implements
higher-order B-spline bases spanning function spaces without
eigen-decomposition. Feedforward network architecture means that the system is
amenable to end-to-end neural learning. The key to our network is space-varying
convolution, similar to a graph operator, whose weights are computed from the
known 3D geometry for normalization. We show that the number of layers in the
normalization at the encoder is equivalent to the number of terms in a matrix
inverse Taylor series. Experimental results on real-world 3D point clouds show
up to 2-3 dB gain over RAHT in energy compaction and 20-30% bitrate reduction.

通过使用体积法进行 3D 点云属性压缩，本研究介绍了一种新的前馈线性网络方法 ——B 样条基的非特征值分解实现，利用空间变化的卷积和编码器中表示矩阵逆泰勒级数的项的数目等因素，能够在真实的 3D 点云场景下，具有比区域自适应分层变换（RAHT）更好的能量压缩效果，并能减少 20-30% 的比特率。

使用带有几何注意力的前向网络进行三维点云体属性压缩

Volumetric Attribute Compression for 3D Point Clouds using Feedforward  Network with Geometric Attention

Recent progress in neural learning demonstrated that machines can do well in
regularized tasks, e.g., the game of Go. However, artistic activities such as
poem generation are still widely regarded as human's special capability. In
this paper, we demonstrate that a simple neural model can imitate human in some
tasks of art generation. We particularly focus on traditional Chinese poetry,
and show that machines can do as well as many contemporary poets and weakly
pass the Feigenbaum Test, a variant of Turing test in professional domains. Our
method is based on an attention-based recurrent neural network, which accepts a
set of keywords as the theme and generates poems by looking at each keyword
during the generation. A number of techniques are proposed to improve the
model, including character vector initialization, attention to input and
hybrid-style training. Compared to existing poetry generation methods, our
model can generate much more theme-consistent and semantic-rich poems.

本文通过基于注意力机制的循环神经网络，利用一组关键字作为主题，生成类似于人类古诗词的诗歌，方法包括字符向量初始化、注重输入和混合式训练等技术。相比于现有的诗歌生成方法，我们的模型生成的诗歌与主题更加一致，语义更加丰富。