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- W4286768400 abstract "Establishing a correspondence between two non-rigidly deforming shapes is one of the most fundamental problems in visual computing. Existing methods often show weak resilience when presented with challenges innate to real-world data such as noise, outliers, self-occlusion etc. On the other hand, auto-decoders have demonstrated strong expressive power in learning geometrically meaningful latent embeddings. However, their use in emph{shape analysis} has been limited. In this paper, we introduce an approach based on an auto-decoder framework, that learns a continuous shape-wise deformation field over a fixed template. By supervising the deformation field for points on-surface and regularising for points off-surface through a novel emph{Signed Distance Regularisation} (SDR), we learn an alignment between the template and shape emph{volumes}. Trained on clean water-tight meshes, emph{without} any data-augmentation, we demonstrate compelling performance on compromised data and real-world scans." @default.
- W4286768400 created "2022-07-23" @default.
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- W4286768400 date "2022-03-15" @default.
- W4286768400 modified "2023-09-24" @default.
- W4286768400 title "Implicit field supervision for robust non-rigid shape matching" @default.
- W4286768400 doi "https://doi.org/10.48550/arxiv.2203.07694" @default.
- W4286768400 hasPublicationYear "2022" @default.
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