PointCNN provides a convolutional neural network architecture designed specifically for processing unordered 3D point cloud data. It applies an X-transformation to learned weights before convolution to account for the permutation invariance of point sets. The framework achieves state-of-the-art results on point cloud benchmarks including ModelNet40 classification, ShapeNet Parts segmentation, and ScanNet semantic segmentation.