  The contactless estimation of the weight of a container and the amount of its
content manipulated by a person are key pre-requisites for safe human-to-robot
handovers. However, opaqueness and transparencies of the container and the
content, and variability of materials, shapes, and sizes, make this estimation
difficult. In this paper, we present a range of methods and an open framework
to benchmark acoustic and visual perception for the estimation of the capacity
of a container, and the type, mass, and amount of its content. The framework
includes a dataset, specific tasks and performance measures. We conduct an
in-depth comparative analysis of methods that used this framework and
audio-only or vision-only baselines designed from related works. Based on this
analysis, we can conclude that audio-only and audio-visual classifiers are
suitable for the estimation of the type and amount of the content using
different types of convolutional neural networks, combined with either
recurrent neural networks or a majority voting strategy, whereas computer
vision methods are suitable to determine the capacity of the container using
regression and geometric approaches. Classifying the content type and level
using only audio achieves a weighted average F1-score up to 81% and 97%,
respectively. Estimating the container capacity with vision-only approaches and
estimating the filling mass with audio-visual multi-stage approaches reach up
to 65% weighted average capacity and mass scores. These results show that there
is still room for improvement on the design of new methods. These new methods
can be ranked and compared on the individual leaderboards provided by our open
framework.
