  Learning 3D scene representation from a single-view image is a long-standing
fundamental problem in computer vision, with the inherent ambiguity in
predicting contents unseen from the input view. Built on the recently proposed
3D Gaussian Splatting (3DGS), the Splatter Image method has made promising
progress on fast single-image novel view synthesis via learning a single 3D
Gaussian for each pixel based on the U-Net feature map of an input image.
However, it has limited expressive power to represent occluded components that
are not observable in the input view. To address this problem, this paper
presents a Hierarchical Splatter Image method in which a pixel is worth more
than one 3D Gaussians. Specifically, each pixel is represented by a parent 3D
Gaussian and a small number of child 3D Gaussians. Parent 3D Gaussians are
learned as done in the vanilla Splatter Image. Child 3D Gaussians are learned
via a lightweight Multi-Layer Perceptron (MLP) which takes as input the
projected image features of a parent 3D Gaussian and the embedding of a target
camera view. Both parent and child 3D Gaussians are learned end-to-end in a
stage-wise way. The joint condition of input image features from eyes of the
parent Gaussians and the target camera position facilitates learning to
allocate child Gaussians to ``see the unseen'', recovering the occluded details
that are often missed by parent Gaussians.
  In experiments, the proposed method is tested on the ShapeNet-SRN and CO3D
datasets with state-of-the-art performance obtained, especially showing
promising capabilities of reconstructing occluded contents in the input view.
