When Turing Award Honoree Dr. Geoffrey Hinton speaks, the AI community listens. Last week, Hinton tweeted, “Finding the natural parts of an object and their intrinsic coordinate frames without supervision is a crucial step in learning to parse images into part-whole hierarchies. If we start with point clouds, we can do it!“
The comments came with the publication of Canonical Capsules: Unsupervised Capsules in Canonical Pose, a new paper from Hinton and a team of researchers at University of British Columbia, University of Toronto, Google Research and University of Victoria, that proposes an architecture for unsupervised learning with 3D point clouds based on capsules.
The paper Canonical Capsules: Unsupervised Capsules in Canonical Pose is on arXiv, and researchers will release the code and dataset soon.