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We explore a new idea for learning based shape reconstruction from a pointcloud, based on the recently popularized implicit neural shape representations.We cast the problem as a few-shot learning of implicit neural signed distancefunctions in feature space, that we approach using gradient basedmeta-learning. We use a convolutional encoder to build a feature space giventhe input point cloud. An implicit decoder learns to predict signed distancevalues given points represented in this feature space. Setting the input pointcloud, i.e. samples from the target shape function's zero level set, as thesupport (i.e. context) in few-shot learning terms, we train the decoder suchthat it can adapt its weights to the underlying shape of this context with afew (5) tuning steps. We thus combine two types of implicit neural networkconditioning mechanisms simultaneously for the first time, namely featureencoding and meta-learning. Our numerical and qualitative evaluation shows thatin the context of implicit reconstruction from a sparse point cloud, ourproposed strategy, i.e. meta-learning in feature space, outperforms existingalternatives, namely standard supervised learning in feature space, andmeta-learning in euclidean space, while still providing fast inference.

Few 'Zero Level Set'-Shot Learning of Shape Signed Distance Functions in Feature Space