From a single image, humans are able to perceive the full 3D shape of an
object by exploiting learned shape priors from everyday life. Contemporary
single-image 3D reconstruction algorithms aim to solve this task in a similar
fashion, but often end up with priors that are highly biased by training
classes. Here we present an algorithm, Generalizable Reconstruction (GenRe),
designed to capture more generic, class-agnostic shape priors. We achieve this
with an inference network and training procedure that combine 2.5D
representations of visible surfaces (depth and silhouette), spherical shape
representations of both visible and non-visible surfaces, and 3D voxel-based
representations, in a principled manner that exploits the causal structure of
how 3D shapes give rise to 2D images. Experiments demonstrate that GenRe
performs well on single-view shape reconstruction, and generalizes to diverse
novel objects from categories not seen during training.

本研究提出一种通用重建算法（GenRe），利用深度和轮廓等 2.5D 表示方法和球形和体素表示方法，旨在捕捉更通用，不受特定类别约束的形状先验知识，实验结果表明该算法可以很好地解决从单张图像还原 3D 形状，并且能够泛化到未在训练中见过的多种不同类别的新物体。