  Transformer plays an increasingly important role in various computer vision
areas and remarkable achievements have also been made in point cloud analysis.
Since they mainly focus on point-wise transformer, an adaptive channel encoding
transformer is proposed in this paper. Specifically, a channel convolution
called Transformer-Conv is designed to encode the channel. It can encode
feature channels by capturing the potential relationship between coordinates
and features. Compared with simply assigning attention weight to each channel,
our method aims to encode the channel adaptively. In addition, our network
adopts the neighborhood search method of low-level and high-level dual semantic
receptive fields to improve the performance. Extensive experiments show that
our method is superior to state-of-the-art point cloud classification and
segmentation methods on three benchmark datasets.
