@online{Zhou2102.01161,
TITLE = {Adjoint Rigid Transform Network: {T}ask-conditioned Alignment of {3D} Shapes},
AUTHOR = {Zhou, Keyang and Bhatnagar, Bharat Lal and Schiele, Bernt and Pons-Moll, Gerard},
LANGUAGE = {eng},
URL = {https://arxiv.org/abs/2102.01161},
EPRINT = {2102.01161},
EPRINTTYPE = {arXiv},
YEAR = {2021},
ABSTRACT = {Most learning methods for 3D data (point clouds, meshes) suffer significant<br>performance drops when the data is not carefully aligned to a canonical<br>orientation. Aligning real world 3D data collected from different sources is<br>non-trivial and requires manual intervention. In this paper, we propose the<br>Adjoint Rigid Transform (ART) Network, a neural module which can be integrated<br>with a variety of 3D networks to significantly boost their performance. ART<br>learns to rotate input shapes to a learned canonical orientation, which is<br>crucial for a lot of tasks such as shape reconstruction, interpolation,<br>non-rigid registration, and latent disentanglement. ART achieves this with<br>self-supervision and a rotation equivariance constraint on predicted rotations.<br>The remarkable result is that with only self-supervision, ART facilitates<br>learning a unique canonical orientation for both rigid and nonrigid shapes,<br>which leads to a notable boost in performance of aforementioned tasks. We will<br>release our code and pre-trained models for further research.<br>},
}