  Self-supervised learning is attracting large attention in point cloud
understanding. However, exploring discriminative and transferable features
still remains challenging due to their nature of irregularity and sparsity. We
propose a geometrically and adaptively masked auto-encoder for self-supervised
learning on point clouds, termed \textit{PointGame}. PointGame contains two
core components: GATE and EAT. GATE stands for the geometrical and adaptive
token embedding module; it not only absorbs the conventional wisdom of
geometric descriptors that captures the surface shape effectively, but also
exploits adaptive saliency to focus on the salient part of a point cloud. EAT
stands for the external attention-based Transformer encoder with linear
computational complexity, which increases the efficiency of the whole pipeline.
Unlike cutting-edge unsupervised learning models, PointGame leverages geometric
descriptors to perceive surface shapes and adaptively mines discriminative
features from training data. PointGame showcases clear advantages over its
competitors on various downstream tasks under both global and local fine-tuning
strategies. The code and pre-trained models will be publicly available.
