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Point signature, a representation describing the structural neighborhood of apoint in 3D shapes, can be applied to establish correspondences between pointsin 3D shapes. Conventional methods apply a weight-sharing network, e.g., anykind of graph neural networks, across all neighborhoods to directly generatepoint signatures and gain the generalization ability by extensive training overa large amount of training samples from scratch. However, these methods lackthe flexibility in rapidly adapting to unseen neighborhood structures and thusgeneralizes poorly on new point sets. In this paper, we propose a novelmeta-learning based 3D point signature model, named 3Dmetapointsignature (MEPS)network, that is capable of learning robust point signatures in 3D shapes. Byregarding each point signature learning process as a task, our method obtainsan optimized model over the best performance on the distribution of all tasks,generating reliable signatures for new tasks, i.e., signatures of unseen pointneighborhoods. Specifically, the MEPS consists of two modules: a base signaturelearner and a meta signature learner. During training, the base-learner istrained to perform specific signature learning tasks. In the meantime, themeta-learner is trained to update the base-learner with optimal parameters.During testing, the meta-learner that is learned with the distribution of alltasks can adaptively change parameters of the base-learner, accommodating tounseen local neighborhoods. We evaluate the MEPS model on two datasets, e.g.,FAUST and TOSCA, for dense 3Dshape correspondence. Experimental resultsdemonstrate that our method not only gains significant improvements over thebaseline model and achieves state-of-the-art results, but also is capable ofhandling unseen 3D shapes.

3D Meta Point Signature: Learning to Learn 3D Point Signature for 3D Dense Shape Correspondence