  Time-of-Flight (ToF) cameras are becoming popular in a wide span of areas
ranging from consumer-grade electronic devices to safety-critical industrial
robots. This is mainly due to their high frame rate, relative good precision
and the lowered costs. Although ToF cameras are in continuous development,
especially pulse-based variants, they still face different problems, including
spurious noise over the points or multipath inference (MPI). The latter can
cause deformed surfaces to manifest themselves on curved surfaces instead of
planar ones, making standard spatial data preprocessing, such as plane
extraction, difficult. In this paper, we focus on the MPI reduction problem
using Feature Pyramid Networks (FPN) which allow the mitigation of this type of
artifact for pulse-based ToF cameras. With our end-to-end network, we managed
to attenuate the MPI effect on planar surfaces using a learning-based method on
real ToF data. Both the custom dataset used for our model training as well as
the code is available on the author's Github homepage.
