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

The field of 3D detailed human mesh reconstruction has made significant
progress in recent years. However, current methods still face challenges when
used in industrial applications due to unstable results, low-quality meshes,
and a lack of UV unwrapping and skinning weights. In this paper, we present
SHERT, a novel pipeline that can reconstruct semantic human meshes with
textures and high-precision details. SHERT applies semantic- and normal-based
sampling between the detailed surface (eg mesh and SDF) and the corresponding
SMPL-X model to obtain a partially sampled semantic mesh and then generates the
complete semantic mesh by our specifically designed self-supervised completion
and refinement networks. Using the complete semantic mesh as a basis, we employ
a texture diffusion model to create human textures that are driven by both
images and texts. Our reconstructed meshes have stable UV unwrapping,
high-quality triangle meshes, and consistent semantic information. The given
SMPL-X model provides semantic information and shape priors, allowing SHERT to
perform well even with incorrect and incomplete inputs. The semantic
information also makes it easy to substitute and animate different body parts
such as the face, body, and hands. Quantitative and qualitative experiments
demonstrate that SHERT is capable of producing high-fidelity and robust
semantic meshes that outperform state-of-the-art methods.

SHERT 是一个新的流程，可以通过语义和法线采样来重构具有纹理和高精度细节的语义人体网格，生成稳定的 UV 展开、高质量的三角网格以及一致的语义信息，优于最先进的方法。