Adjoint Rigid Transform Network: Self-supervised Alignment of 3D Shapes
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Most learning methods for 3D data (point clouds, meshes) suffer significant performance drops when the data is not carefully aligned to a canonical orientation. Aligning real world 3D data collected from different sources is non-trivial and requires manual intervention. In this paper, we propose the Adjoint Rigid Transform (ART) Network, a neural module which can be integrated with existing 3D networks to significantly boost their performance in tasks such as shape reconstruction, non-rigid registration, and latent disentanglement. ART learns to rotate input shapes to a canonical orientation that is crucial for a lot of tasks. ART achieves this by imposing rotation equivariance constraint on input shapes. The remarkable result is that with only self-supervision, ART can discover a unique canonical orientation for both rigid and nonrigid objects, which leads to a notable boost in downstream task performance. We will release our code and pre-trained models for further research.
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