MLP-Mixer has newly appeared as a new challenger against the realm of CNNs
and transformer. Despite its simplicity compared to transformer, the concept of
channel-mixing MLPs and token-mixing MLPs achieves noticeable performance in
visual recognition tasks. Unlike images, point clouds are inherently sparse,
unordered and irregular, which limits the direct use of MLP-Mixer for point
cloud understanding. In this paper, we propose PointMixer, a universal point
set operator that facilitates information sharing among unstructured 3D points.
By simply replacing token-mixing MLPs with a softmax function, PointMixer can
"mix" features within/between point sets. By doing so, PointMixer can be
broadly used in the network as inter-set mixing, intra-set mixing, and pyramid
mixing. Extensive experiments show the competitive or superior performance of
PointMixer in semantic segmentation, classification, and point reconstruction
against transformer-based methods.Comment: Accepted to ECCV 202
