  This paper presents a fully automatic framework for extracting editable 3D
objects directly from a single photograph. Unlike previous methods which
recover either depth maps, point clouds, or mesh surfaces, we aim to recover 3D
objects with semantic parts and can be directly edited. We base our work on the
assumption that most human-made objects are constituted by parts and these
parts can be well represented by generalized primitives. Our work makes an
attempt towards recovering two types of primitive-shaped objects, namely,
generalized cuboids and generalized cylinders. To this end, we build a novel
instance-aware segmentation network for accurate part separation. Our GeoNet
outputs a set of smooth part-level masks labeled as profiles and bodies. Then
in a key stage, we simultaneously identify profile-body relations and recover
3D parts by sweeping the recognized profile along their body contour and
jointly optimize the geometry to align with the recovered masks. Qualitative
and quantitative experiments show that our algorithm can recover high quality
3D models and outperforms existing methods in both instance segmentation and 3D
reconstruction. The dataset and code of AutoSweep are available at
https://chenxin.tech/AutoSweep.html.
