Plane arrangements are a useful tool for surface and volume modelling.
However, their main drawback is poor scalability. We introduce two key
novelties that enable the construction of plane arrangements for complex
objects and entire scenes: an ordering scheme for the plane insertion and the
direct use of input points during arrangement construction. Both ingredients
reduce the number of unwanted splits, resulting in improved scalability of the
construction mechanism by up to two orders of magnitude compared to existing
algorithms. We further introduce a remeshing and simplification technique that
allows us to extract low-polygon surface meshes and lightweight convex
decompositions of volumes from the arrangement. We show that our approach leads
to state-of-the-art results for the aforementioned tasks by comparing it to
learning-based and traditional approaches on various different datasets. Our
implementation is available at this https URL .

飞机安排在表面和体积建模中是一种有用的工具，本文引入了两个关键创新点，通过平面插入的排序方案和在安排构建过程中直接使用输入点，减少了不必要的分割，从而将构建机制的可扩展性提高了两个数量级，同时还介绍了一种重构和简化技术，可以从安排中提取低多边形表面网格和轻量级凸体分解。与基于学习和传统方法在不同数据集上进行比较，结果表明我们的方法在上述任务中达到了最先进的效果。

低多边形表面和体填充的简洁平面排列

Concise Plane Arrangements for Low-Poly Surface and Volume Modelling

Surface reconstruction with preservation of geometric features is a
challenging computer vision task. Despite significant progress in implicit
shape reconstruction, state-of-the-art mesh extraction methods often produce
aliased, perceptually distorted surfaces and lack scalability to
high-resolution 3D shapes. We present a data-driven approach for automatic
feature detection and remeshing that requires only a coarse, aliased mesh as
input and scales to arbitrary resolution reconstructions. We define and learn a
collection of surface-based fields to (1) capture sharp geometric features in
the shape with an implicit vertexwise model and (2) approximate improvements in
normals alignment obtained by applying edge-flips with an edgewise model. To
support scaling to arbitrary complexity shapes, we learn our fields using local
triangulated patches, fusing estimates on complete surface meshes. Our feature
remeshing algorithm integrates the learned fields as sharp feature priors and
optimizes vertex placement and mesh connectivity for maximum expected surface
improvement. On a challenging collection of high-resolution shape
reconstructions in the ABC dataset, our algorithm improves over
state-of-the-art by 26% normals F-score and 42% perceptual
$\text{RMSE}_{\text{v}}$.

表面重建中保留几何特征是一项具有挑战性的计算机视觉任务，我们提出了一种数据驱动的方法，自动检测和重新排列特征，以实现对高分辨率 3D 形状的可伸缩重建。

PRS：面部无分辨率表面重网格化的尖锐特征先验

PRS: Sharp Feature Priors for Resolution-Free Surface Remeshing

Meshes are widely used in 3D computer vision and graphics, but their
irregular topology poses challenges in applying them to existing neural network
architectures. Recent advances in mesh neural networks turn to remeshing and
push the boundary of pioneer methods that solely take the raw meshes as input.
Although the remeshing offers a regular topology that significantly facilitates
the design of mesh network architectures, features extracted from such remeshed
proxies may struggle to retain the underlying geometry faithfully, limiting the
subsequent neural network's capacity. To address this issue, we propose
SieveNet, a novel paradigm that takes into account both the regular topology
and the exact geometry. Specifically, this method utilizes structured mesh
topology from remeshing and accurate geometric information from
distortion-aware point sampling on the surface of the original mesh.
Furthermore, our method eliminates the need for hand-crafted feature
engineering and can leverage off-the-shelf network architectures such as the
vision transformer. Comprehensive experimental results on classification and
segmentation tasks well demonstrate the effectiveness and superiority of our
method.

SieveNet 是一种新的方法，综合考虑了规则拓扑结构、准确的几何信息和网络架构，用于处理 3D 计算机视觉和图形中的网格数据。

SieveNet: 基于点特征的网状网络选择

SieveNet: Selecting Point-Based Features for Mesh Networks

Dr. KID is an algorithm that uses isometric decomposition for the
physicalization of potato-shaped organic models in a puzzle fashion. The
algorithm begins with creating a simple, regular triangular surface mesh of
organic shapes, followed by iterative k-means clustering and remeshing. For
clustering, we need similarity between triangles (segments) which is defined as
a distance function. The distance function maps each triangle's shape to a
single point in the virtual 3D space. Thus, the distance between the triangles
indicates their degree of dissimilarity. K-means clustering uses this distance
and sorts of segments into k classes. After this, remeshing is applied to
minimize the distance between triangles within the same cluster by making their
shapes identical. Clustering and remeshing are repeated until the distance
between triangles in the same cluster reaches an acceptable threshold. We adopt
a curvature-aware strategy to determine the surface thickness and finalize
puzzle pieces for 3D printing. Identical hinges and holes are created for
assembling the puzzle components. For smoother outcomes, we use triangle
subdivision along with curvature-aware clustering, generating curved triangular
patches for 3D printing. Our algorithm was evaluated using various models, and
the 3D-printed results were analyzed. Findings indicate that our algorithm
performs reliably on target organic shapes with minimal loss of input geometry.

Dr. KID 是一种使用等距分解算法的拼图化马铃薯形有机模型物理化的算法，该算法使用 k-means 聚类和 remeshing 迭代重构，通过曲率感知策略确定表面厚度并生成拼图组件，通过三角形子分割和曲率感知聚类实现更顺滑的结果，经过评估，证明在目标有机形状上表现可靠，且几何损失最小化。