  In this paper, we propose a pipeline to generate 3D point cloud of an object
from a single-view RGB image. Most previous work predict the 3D point
coordinates from single RGB images directly. We decompose this problem into
depth estimation from single images and point cloud completion from partial
point clouds.
  Our method sequentially predicts the depth maps from images and then infers
the complete 3D object point clouds based on the predicted partial point
clouds. We explicitly impose the camera model geometrical constraint in our
pipeline and enforce the alignment of the generated point clouds and estimated
depth maps.
  Experimental results for the single image 3D object reconstruction task show
that the proposed method outperforms existing state-of-the-art methods. Both
the qualitative and quantitative results demonstrate the generality and
suitability of our method.
