Implicit Geometric Regularization for Learning Shapes

• URL: http://arxiv.org/abs/2002.10099v2
• Date: Thu, 9 Jul 2020 12:32:45 GMT
• Title: Implicit Geometric Regularization for Learning Shapes
• Authors: Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, Yaron Lipman
• Abstract summary: We offer a new paradigm for computing high fidelity implicit neural representations directly from raw data. We show that our method leads to state of the art implicit neural representations with higher level-of-details and fidelity compared to previous methods.
• Score: 34.052738965233445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Representing shapes as level sets of neural networks has been recently proved to be useful for different shape analysis and reconstruction tasks. So far, such representations were computed using either: (i) pre-computed implicit shape representations; or (ii) loss functions explicitly defined over the neural level sets. In this paper we offer a new paradigm for computing high fidelity implicit neural representations directly from raw data (i.e., point clouds, with or without normal information). We observe that a rather simple loss function, encouraging the neural network to vanish on the input point cloud and to have a unit norm gradient, possesses an implicit geometric regularization property that favors smooth and natural zero level set surfaces, avoiding bad zero-loss solutions. We provide a theoretical analysis of this property for the linear case, and show that, in practice, our method leads to state of the art implicit neural representations with higher level-of-details and fidelity compared to previous methods.

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