  Scene Completion is the task of completing missing geometry from a partial
scan of a scene. Most previous methods compute an implicit representation from
range data using a Truncated Signed Distance Function (T-SDF) computed on a 3D
grid as input to neural networks. The truncation decreases but does not remove
the border errors introduced by the sign of SDF for open surfaces. As an
alternative, we present an Unsigned Distance Function (UDF) as an input
representation to scene completion neural networks. The proposed UDF is simple,
and efficient as a geometry representation, and can be computed on any point
cloud. In contrast to usual Signed Distance Functions, our UDF does not require
normal computation. To obtain the explicit geometry, we present a method for
extracting a point cloud from discretized UDF values on a sparse grid. We
compare different SDFs and UDFs for the scene completion task on indoor and
outdoor point clouds collected using RGB-D and LiDAR sensors and show improved
completion using the proposed UDF function.
