  Self-attention mechanism recently achieves impressive advancement in Natural
Language Processing (NLP) and Image Processing domains. And its permutation
invariance property makes it ideally suitable for point cloud processing.
Inspired by this remarkable success, we propose an end-to-end architecture,
dubbed Cross-Level Cross-Scale Cross-Attention Network (CLCSCANet), for point
cloud representation learning. First, a point-wise feature pyramid module is
introduced to hierarchically extract features from different scales or
resolutions. Then a cross-level cross-attention is designed to model long-range
inter-level and intra-level dependencies. Finally, we develop a cross-scale
cross-attention module to capture interactions between-and-within scales for
representation enhancement. Compared with state-of-the-art approaches, our
network can obtain competitive performance on challenging 3D object
classification, point cloud segmentation tasks via comprehensive experimental
evaluation.
