In this paper, we present a novel deep method to reconstruct a point cloud of
an object from a single still image. Prior arts in the field struggle to
reconstruct an accurate and scalable 3D model due to either the inefficient and
expensive 3D representations, the dependency between the output and number of
model parameters or the lack of a suitable computing operation. We propose to
overcome these by deforming a random point cloud to the object shape through
two steps: feature blending and deformation. In the first step, the global and
point-specific shape features extracted from a 2D object image are blended with
the encoded feature of a randomly generated point cloud, and then this mixture
is sent to the deformation step to produce the final representative point set
of the object. In the deformation process, we introduce a new layer termed as
GraphX that considers the inter-relationship between points like common graph
convolutions but operates on unordered sets. Moreover, with a simple trick, the
proposed model can generate an arbitrary-sized point cloud, which is the first
deep method to do so. Extensive experiments verify that we outperform existing
models and halve the state-of-the-art distance score in single image 3D
reconstruction.

本文提出了一种新颖的深度学习方法，通过两个步骤（特征融合和形变）将随机点云变形成对象形状，从而重建单张图像的点云。通过引入一个新层 GraphX，考虑点之间的相互关系，操作无序集合。这个模型还可以生成任意大小的点云，验证了在单图像 3D 重建上超越了现有模型并将其分数减半。