  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/
