Canonical Capsules: Self-Supervised Capsules in Canonical Pose

Weiwei Sun, Andrea Tagliasacchi, Boyang Deng, Sara Sabour, Soroosh Yazdani, Geoffrey Hinton, Kwang Moo Yi

Keywords: object-centric representation learning, capsules, primary capsules, unsupervised, self-supervised, 3D point clouds

TL;DR: A self-supervised capsule architecture that canonicalizes data while simultaneously decomposing point clouds into parts to perform unsupervised representation learning.

Abstract: We propose a self-supervised capsule architecture for 3D point clouds. We compute capsule decompositions of objects through permutation-equivariant attention, and self-supervise the process by training with pairs of randomly rotated objects. Our key idea is to aggregate the attention masks into semantic keypoints, and use these to supervise a decomposition that satisfies the capsule invariance/equivariance properties. This not only enables the training of a semantically consistent decomposition, but also allows us to learn a canonicalization operation that enables object-centric reasoning. To train our neural network we require neither classification labels nor manually-aligned training datasets. Yet, by learning an object-centric representation in a self-supervised manner, our method outperforms the state-of-the-art on 3D point cloud reconstruction, canonicalization, and unsupervised classification.

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Code: https://github.com/canonical-capsules/canonical-capsules