Generation of 3D data by deep neural network has been attracting increasing
attention in the research community. The majority of extant works resort to
regular representations such as volumetric grids or collection of images;
however, these representations obscure the natural invariance of 3D shapes
under geometric transformations and also suffer from a number of other issues.
In this paper we address the problem of 3D reconstruction from a single image,
generating a straight-forward form of output -- point cloud coordinates. Along
with this problem arises a unique and interesting issue, that the groundtruth
shape for an input image may be ambiguous. Driven by this unorthodox output
form and the inherent ambiguity in groundtruth, we design architecture, loss
function and learning paradigm that are novel and effective. Our final solution
is a conditional shape sampler, capable of predicting multiple plausible 3D
point clouds from an input image. In experiments not only can our system
outperform state-of-the-art methods on single image based 3d reconstruction
benchmarks; but it also shows a strong performance for 3d shape completion and
promising ability in making multiple plausible predictions.

使用深度神经网络从单一图像中重建 3D 点云坐标，设计了面对真实世界几何转换不变性和地面真实性模糊的问题的新型方法，包括条件形状采样器，能够预测多个可能的 3D 点云。在实验中表现优异，不仅在单图像 based 3D 重建基准测试中胜过现有技术，也在形状补全方面表现出强大性能，有望在多个可能性预测方面表现出色。