  Discovering inter-point connection for efficient high-dimensional feature
extraction from point coordinate is a key challenge in processing point cloud.
Most existing methods focus on designing efficient local feature extractors
while ignoring global connection, or vice versa. In this paper, we design a new
Inductive Bias-aided Transformer (IBT) method to learn 3D inter-point
relations, which considers both local and global attentions. Specifically,
considering local spatial coherence, local feature learning is performed
through Relative Position Encoding and Attentive Feature Pooling. We
incorporate the learned locality into the Transformer module. The local feature
affects value component in Transformer to modulate the relationship between
channels of each point, which can enhance self-attention mechanism with
locality based channel interaction. We demonstrate its superiority
experimentally on classification and segmentation tasks. The code is available
at: https://github.com/jiamang/IBT
