The CSN pipeline:Left: Given an input shape collection, our method constructs a graph where each shape is represented
as a node and edges indicate shape pairs that are deemed compatible for cross-shape feature propagation.
Middle: Our network is designed to compute point-wise feature representations for a given shape
(grey shape) by enabling interactions between its own point-wise features and those of other shapes using
our cross-shape attention mechanism. Right: As a result, the point-wise features of the shape
become more synchronized with ones of other relevant shapes leading to more accurate fine-grained segmentation.
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.
Cross-Shape Attention for Part Segmentation of 3D Point Clouds