A Novel Framework for Learning Bézier Decomposition From 3D Point Clouds

This paper proposes a fully differentiable and end-to-end framework for learning Bézier decomposition on 3D point clouds. The framework aims to partition input point clouds into multiple Bézier primitive patches through a learned Bézier decomposition process. Unlike previous approaches that handle different primitive types separately, thus being limited to specific shape categories, our method seeks to achieve a generalized primitive segmentation on point clouds. Drawing inspiration from Bézier decomposition on NURBS models, we adapt it to guide point cloud segmentation without relying on pre-defined primitive types. To achieve this, we introduce a joint optimization framework that simultaneously learns Bézier primitive segmentation and geometric fitting in a cascaded architecture. Additionally, we propose a soft voting regularizer to enhance primitive segmentation and an auto-weight embedding module to effectively cluster point features, making the network more robust and applicable to various scenarios. Furthermore, we incorporate a reconstruction module capable of processing multiple CAD models with different primitives simultaneously. Extensive experiments were conducted on both synthetic ABC datasets and real-scan datasets to validate and compare our approach against several baseline methods. The results demonstrate that our method outperforms previous work in terms of segmentation accuracy, while also exhibiting significantly faster inference speed.