PFMixer: Point Cloud Frequency Mixing

Convolutional Neural Networks and Transformer are popu-lar for various computer vision tasks. Recently, a modern multi-layer perceptrons (MLPs) architecture, MLP-Mixer, has achieved remarkable performance in visual recognition tasks. MLP-Mixer includes two types of MLPs: channel-mixing and token-mixing MLPs. Inspired by its simplicity and success, we introduce the application of MLP-Mixer for point cloud processing. We design token-mixing operations to allow interaction between high-frequency (edge) regions and low-frequency (smooth) regions. Furthermore, channel-mixing operations are proposed to allow interaction between different channels of local structures and global shapes. Ex-tensive experiments demonstrate the superiority of PFMixer: POINT CLOUD FREQUENCY MIXING