  The task of learning from point cloud data is always challenging due to the
often occurrence of noise and outliers in the data. Such data inaccuracies can
significantly influence the performance of state-of-the-art deep learning
networks and their ability to classify or segment objects. While there are some
robust deep learning approaches, they are computationally too expensive for
real-time applications. This paper proposes a deep learning solution that
includes a novel robust pooling layer which greatly enhances network robustness
and performs significantly faster than state-of-the-art approaches. The
proposed pooling layer looks for data a mode/cluster using two methods, RANSAC,
and histogram, as clusters are indicative of models. We tested the pooling
layer into frameworks such as Point-based and graph-based neural networks, and
the tests showed enhanced robustness as compared to robust state-of-the-art
methods.
