Abstract
This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds.
The SO-Net models the spatial distribution of point cloud
by building a Self-Organizing Map (SOM). Based on the
SOM, SO-Net performs hierarchical feature extraction on
individual points and SOM nodes, and ultimately represents
the input point cloud by a single feature vector. The receptive field of the network can be systematically adjusted
by conducting point-to-node k nearest neighbor search. In
recognition tasks such as point cloud reconstruction, classification, object part segmentation and shape retrieval, our
proposed network demonstrates performance that is similar
with or better than state-of-the-art approaches. In addition,
the training speed is significantly faster than existing point
cloud recognition networks because of the parallelizability
and simplicity of the proposed architecture. Our code is
available at the project website