Reasoning 3D shapes from 2D images is an essential yet challenging task,
especially when only single-view images are at our disposal. While an object
can have a complicated shape, individual parts are usually close to geometric
primitives and thus are easier to model. Furthermore, parts provide a mid-level
representation that is robust to appearance variations across objects in a
particular category. In this work, we tackle the problem of 3D part discovery
from only 2D image collections. Instead of relying on manually annotated parts
for supervision, we propose a self-supervised approach, latent part discovery
(LPD). Our key insight is to learn a novel part shape prior that allows each
part to fit an object shape faithfully while constrained to have simple
geometry. Extensive experiments on the synthetic ShapeNet, PartNet, and
real-world Pascal 3D+ datasets show that our method discovers consistent object
parts and achieves favorable reconstruction accuracy compared to the existing
methods with the same level of supervision.

本文提出了一种自我监督的方法（LPD）来发现只有 2D 图像的物体的三维部件，并使用新的部件形状先验学习足够简单而又能忠实地匹配物体形状的部件，这种方法在实验中表现良好，比现有方法具有更好的重构精度。