  Recent years have witnessed the great success of deep learning on various
point cloud analysis tasks, e.g., classification and semantic segmentation.
Since point cloud data is sparse and irregularly distributed, one key issue for
point cloud data processing is extracting useful information from local
regions. To achieve this, previous works mainly extract the points' features
from local regions by learning the relation between each pair of adjacent
points. However, these works ignore the relation between edges in local
regions, which encodes the local shape information. Associating the
neighbouring edges could potentially make the point-to-point relation more
aware of the local structure and more robust. To explore the role of the
relation between edges, this paper proposes a novel Adaptive Edge-to-Edge
Interaction Learning module, which aims to enhance the point-to-point relation
through modelling the edge-to-edge interaction in the local region adaptively.
We further extend the module to a symmetric version to capture the local
structure more thoroughly. Taking advantage of the proposed modules, we develop
two networks for segmentation and shape classification tasks, respectively.
Various experiments on several public point cloud datasets demonstrate the
effectiveness of our method for point cloud analysis.
