  We present a deep learning method that propagates point-wise feature
representations across shapes within a collection for the purpose of 3D shape
segmentation. We propose a cross-shape attention mechanism to enable
interactions between a shape's point-wise features and those of other shapes.
The mechanism assesses both the degree of interaction between points and also
mediates feature propagation across shapes, improving the accuracy and
consistency of the resulting point-wise feature representations for shape
segmentation. Our method also proposes a shape retrieval measure to select
suitable shapes for cross-shape attention operations for each test shape. Our
experiments demonstrate that our approach yields state-of-the-art results in
the popular PartNet dataset.
