We introduce a novel method for acquiring boundary representations (B-Reps)
of 3D CAD models which involves a two-step process: it first applies a spatial
partitioning, referred to as the ``split``, followed by a ``fit`` operation to
derive a single primitive within each partition. Specifically, our partitioning
aims to produce the classical Voronoi diagram of the set of ground-truth (GT)
B-Rep primitives. In contrast to prior B-Rep constructions which were
bottom-up, either via direct primitive fitting or point clustering, our
Split-and-Fit approach is top-down and structure-aware, since a Voronoi
partition explicitly reveals both the number of and the connections between the
primitives. We design a neural network to predict the Voronoi diagram from an
input point cloud or distance field via a binary classification. We show that
our network, coined NVD-Net for neural Voronoi diagrams, can effectively learn
Voronoi partitions for CAD models from training data and exhibits superior
generalization capabilities. Extensive experiments and evaluation demonstrate
that the resulting B-Reps, consisting of parametric surfaces, curves, and
vertices, are more plausible than those obtained by existing alternatives, with
significant improvements in reconstruction quality. Code will be released on
this https URL

我们介绍了一种新的方法来获取 3D CAD 模型的边界表示（B-Reps），这涉及两个步骤：首先应用空间划分，称为 ``拆分 ''，然后通过`` 拟合 '' 操作在每个划分内导出一个单一的基元。我们的划分旨在产生一组地面真值（GT）B-Rep 基元的经典的 Voronoi 图；我们的方法采用自上而下的结构感知方式，能够明确显示基元的数量和连接关系。我们设计了一个神经网络，通过二进制分类从输入点云或距离场预测 Voronoi 图。我们展示了我们的网络 NVD-Net（神经 Voronoi 图）可以有效地从训练数据学习 CAD 模型的 Voronoi 划分，并具有更好的泛化能力。广泛的实验和评估表明，与现有的替代方法相比，所得到的由参数曲面，曲线和顶点组成的 B-Reps 更加合理，重建质量有了显著的提高。代码将在此 https URL 上发布。

分割与匹配：通过结构感知的 Voronoi 分区学习 B-Reps

Split-and-Fit: Learning B-Reps via Structure-Aware Voronoi Partitioning

Boundary Representations (B-Reps) are the industry standard in 3D Computer
Aided Design/Manufacturing (CAD/CAM) and industrial design due to their
fidelity in representing stylistic details. However, they have been ignored in
the 3D style research. Existing 3D style metrics typically operate on meshes or
pointclouds, and fail to account for end-user subjectivity by adopting fixed
definitions of style, either through crowd-sourcing for style labels or
hand-crafted features. We propose UVStyle-Net, a style similarity measure for
B-Reps that leverages the style signals in the second order statistics of the
activations in a pre-trained (unsupervised) 3D encoder, and learns their
relative importance to a subjective end-user through few-shot learning. Our
approach differs from all existing data-driven 3D style methods since it may be
used in completely unsupervised settings, which is desirable given the lack of
publicly available labelled B-Rep datasets. More importantly, the few-shot
learning accounts for the inherent subjectivity associated with style. We show
quantitatively that our proposed method with B-Reps is able to capture stronger
style signals than alternative methods on meshes and pointclouds despite its
significantly greater computational efficiency. We also show it is able to
generate meaningful style gradients with respect to the input shape, and that
few-shot learning with as few as two positive examples selected by an end-user
is sufficient to significantly improve the style measure. Finally, we
demonstrate its efficacy on a large unlabeled public dataset of CAD models.
Source code and data will be released in the future.

提出了一种利用 UVStyle-Net 的 Few-Shot Learning 来度量 B-Reps 中的风格相似性，根据少量正样本学习相对重要性以适应主观的最终用户，并在 CAD 模型的大型未标记公共数据集上展示其功效。