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- W4313172691 abstract "We introduce an unsupervised technique for encoding point clouds into a canonical shape representation, by disentangling shape and pose. Our encoder is stable and consistent, meaning that the shape encoding is purely pose-invariant, while the extracted rotation and translation are able to semantically align different input shapes of the same class to a common canonical pose. Specifically, we design an auto-encoder based on Vector Neuron Networks, a rotation-equivariant neural network, whose layers we extend to provide translation-equivariance in addition to rotation-equivariance only. The resulting encoder produces pose-invariant shape encoding by construction, enabling our approach to focus on learning a consistent canonical pose for a class of objects. Quantitative and qualitative experiments validate the superior stability and consistency of our approach." @default.
- W4313172691 created "2023-01-06" @default.
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- W4313172691 date "2022-01-01" @default.
- W4313172691 modified "2023-09-25" @default.
- W4313172691 title "Shape-Pose Disentanglement Using SE(3)-Equivariant Vector Neurons" @default.
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- W4313172691 doi "https://doi.org/10.1007/978-3-031-20062-5_27" @default.
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