Shape abstraction is an important task for simplifying complex geometric structures while retaining essential features. Sweep surfaces, commonly found in human-made objects, aid in this process by effectively capturing and representing object geometry, thereby facilitating abstraction. In this paper, we introduce \papername, a novel approach to shape abstraction through sweep surfaces. We propose an effective parameterization for sweep surfaces, utilizing superellipses for profile representation and B-spline curves for the axis. This compact representation, requiring as few as 14 float numbers, facilitates intuitive and interactive editing while preserving shape details effectively. Additionally, by introducing a differentiable neural sweeper and an encoder-decoder architecture, we demonstrate the ability to predict sweep surface representations without supervision. We show the superiority of our model through several quantitative and qualitative experiments throughout the paper. Our code is available at https://mingrui-zhao.github.io/SweepNet/

翻译：形状抽象是一项重要任务，旨在简化复杂几何结构的同时保留其本质特征。扫描曲面常见于人造物体中，通过有效捕捉和表示物体几何形态来辅助抽象过程。本文提出一种基于扫描曲面的新型形状抽象方法。我们设计了一种高效的扫描曲面参数化方案，采用超椭圆进行截面轮廓表示，并利用B-spline曲线描述扫描轴线。这种紧凑表示仅需14个浮点数即可实现，在有效保持形状细节的同时支持直观的交互式编辑。此外，通过引入可微分神经扫描器与编码器-解码器架构，我们实现了无监督条件下的扫描曲面表示预测。本文通过多项定量与定性实验证明了该模型的优越性。代码已开源：https://mingrui-zhao.github.io/SweepNet/