  While many works focus on 3D reconstruction from images, in this paper, we
focus on 3D shape reconstruction and completion from a variety of 3D inputs,
which are deficient in some respect: low and high resolution voxels, sparse and
dense point clouds, complete or incomplete. Processing of such 3D inputs is an
increasingly important problem as they are the output of 3D scanners, which are
becoming more accessible, and are the intermediate output of 3D computer vision
algorithms. Recently, learned implicit functions have shown great promise as
they produce continuous reconstructions. However, we identified two limitations
in reconstruction from 3D inputs: 1) details present in the input data are not
retained, and 2) poor reconstruction of articulated humans. To solve this, we
propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs,
can handle multiple topologies, and complete shapes for missing or sparse input
data retaining the nice properties of recent learned implicit functions, but
critically they can also retain detail when it is present in the input data,
and can reconstruct articulated humans. Our work differs from prior work in two
crucial aspects. First, instead of using a single vector to encode a 3D shape,
we extract a learnable 3-dimensional multi-scale tensor of deep features, which
is aligned with the original Euclidean space embedding the shape. Second,
instead of classifying x-y-z point coordinates directly, we classify deep
features extracted from the tensor at a continuous query point. We show that
this forces our model to make decisions based on global and local shape
structure, as opposed to point coordinates, which are arbitrary under Euclidean
transformations. Experiments demonstrate that IF-Nets clearly outperform prior
work in 3D object reconstruction in ShapeNet, and obtain significantly more
accurate 3D human reconstructions.
