This paper proposes BPNet, a novel end-to-end deep learning framework to
learn B\'ezier primitive segmentation on 3D point clouds. The existing works
treat different primitive types separately, thus limiting them to finite shape
categories. To address this issue, we seek a generalized primitive segmentation
on point clouds. Taking inspiration from B\'ezier decomposition on NURBS
models, we transfer it to guide point cloud segmentation casting off primitive
types. A joint optimization framework is proposed to learn B\'ezier primitive
segmentation and geometric fitting simultaneously on a cascaded architecture.
Specifically, we introduce a soft voting regularizer to improve primitive
segmentation and propose an auto-weight embedding module to cluster point
features, making the network more robust and generic. We also introduce a
reconstruction module where we successfully process multiple CAD models with
different primitives simultaneously. We conducted extensive experiments on the
synthetic ABC dataset and real-scan datasets to validate and compare our
approach with different baseline methods. Experiments show superior performance
over previous work in terms of segmentation, with a substantially faster
inference speed.

本论文提出了 BPNet，一种新颖的端到端深度学习框架，用于学习在 3D 点云上的 Bézier 基元分割。通过将 Bézier 分解应用于点云分割，我们能够实现有限形状类别的广义基元分割。我们提出了一个联合优化框架，在级联架构上同时学习 Bézier 基元分割和几何拟合。通过引入软投票正则化器和自动权重嵌入模块，我们使网络更具鲁棒性和通用性。我们还引入了一个重构模块，通过该模块可以同时处理具有不同基元的多个 CAD 模型。我们在合成的 ABC 数据集和实际扫描数据集上进行了大量实验证实并与不同的基准方法进行了比较。实验证明，我们的方法在分割方面具有卓越的性能，并且推理速度显著得更快。

BPNet：三维点云的贝塞尔基元分割

BPNet: Bézier Primitive Segmentation on 3D Point Clouds

This paper introduces HPNet, a novel deep-learning approach for segmenting a
3D shape represented as a point cloud into primitive patches. The key to deep
primitive segmentation is learning a feature representation that can separate
points of different primitives. Unlike utilizing a single feature
representation, HPNet leverages hybrid representations that combine one learned
semantic descriptor, two spectral descriptors derived from predicted geometric
parameters, as well as an adjacency matrix that encodes sharp edges. Moreover,
instead of merely concatenating the descriptors, HPNet optimally combines
hybrid representations by learning combination weights. This weighting module
builds on the entropy of input features. The output primitive segmentation is
obtained from a mean-shift clustering module. Experimental results on benchmark
datasets ANSI and ABCParts show that HPNet leads to significant performance
gains from baseline approaches.

本文介绍了一种用于将以点云表示的三维形状分割成原始补丁的新型深度学习方法 HPNet，其利用混合表示结合学习语义描述符、预测几何参数导出的两个谱描述符以及编码锐利边缘的邻接矩阵。实验结果表明，相较于基线方法，HPNet 可以显著提高性能。