  Recent development of neural implicit function has shown tremendous success
on high-quality 3D shape reconstruction. However, most works divide the space
into inside and outside of the shape, which limits their representing power to
single-layer and watertight shapes. This limitation leads to tedious data
processing (converting non-watertight raw data to watertight) as well as the
incapability of representing general object shapes in the real world. In this
work, we propose a novel method to represent general shapes including
non-watertight shapes and shapes with multi-layer surfaces. We introduce
General Implicit Function for 3D Shape (GIFS), which models the relationships
between every two points instead of the relationships between points and
surfaces. Instead of dividing 3D space into predefined inside-outside regions,
GIFS encodes whether two points are separated by any surface. Experiments on
ShapeNet show that GIFS outperforms previous state-of-the-art methods in terms
of reconstruction quality, rendering efficiency, and visual fidelity. Project
page is available at https://jianglongye.com/gifs .
