  In 2D image processing, some attempts decompose images into high and low
frequency components for describing edge and smooth parts respectively.
Similarly, the contour and flat area of 3D objects, such as the boundary and
seat area of a chair, describe different but also complementary geometries.
However, such investigation is lost in previous deep networks that understand
point clouds by directly treating all points or local patches equally. To solve
this problem, we propose Geometry-Disentangled Attention Network (GDANet).
GDANet introduces Geometry-Disentangle Module to dynamically disentangle point
clouds into the contour and flat part of 3D objects, respectively denoted by
sharp and gentle variation components. Then GDANet exploits Sharp-Gentle
Complementary Attention Module that regards the features from sharp and gentle
variation components as two holistic representations, and pays different
attentions to them while fusing them respectively with original point cloud
features. In this way, our method captures and refines the holistic and
complementary 3D geometric semantics from two distinct disentangled components
to supplement the local information. Extensive experiments on 3D object
classification and segmentation benchmarks demonstrate that GDANet achieves the
state-of-the-arts with fewer parameters. Code is released on
https://github.com/mutianxu/GDANet.
