SAL: Sign Agnostic Learning of Shapes from Raw Data

CVPR 2020 Matan AtzmonYaron Lipman

Recently, neural networks have been used as implicit representations for surface reconstruction, modelling, learning, and generation. So far, training neural networks to be implicit representations of surfaces required training data sampled from a ground-truth signed implicit functions such as signed distance or occupancy functions, which are notoriously hard to compute... (read more)

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