Canonical Capsules: Self-Supervised Capsules in Canonical Pose
Authors: Weiwei Sun, Andrea Tagliasacchi, Boyang Deng, Sara Sabour, Soroosh Yazdani, Geoffrey E. Hinton, Kwang Moo Yi
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. ... Section 4 is dedicated to Experiments. |
| Researcher Affiliation | Collaboration | 1University of British Columbia, 2University of Toronto, 3Google Research, 4University of Victoria, equal contributions |
| Pseudocode | No | The paper describes algorithmic steps and equations but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | In addition to the public code release that we will do, we have included the code in the supplementary |
| Open Datasets | Yes | To evaluate our method, we rely on the Shape Net (Core) dataset [3]2. ... [3] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An Information-Rich 3D Model Repository. ar Xiv Preprint, 2015. |
| Dataset Splits | Yes | We also use the same splits as in Atlas Net V2 [12]: 31747 shapes in the train, and 7943 shapes in the test set. |
| Hardware Specification | Yes | We train each model on a single NVidia V100 GPU. |
| Software Dependencies | No | The paper mentions 'Adam optimizer [29]' but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | For all our experiments we use the Adam optimizer [29] with an initial learning rate of 0.001 and decay rate of 0.1. We train for 325 epochs for the aligned setup to match the Atlas Net V2 [12] original setup. For the unaligned setting, as the problem is harder, we train for a longer number of 450 epochs. We use a batch size of 16. |