DeepCurrents: Learning Implicit Representations of Shapes with Boundaries [article]

David Palmer and Dmitriy Smirnov and Stephanie Wang and Albert Chern and Justin Solomon
2022 arXiv   pre-print
Recent techniques have been successful in reconstructing surfaces as level sets of learned functions (such as signed distance fields) parameterized by deep neural networks. Many of these methods, however, learn only closed surfaces and are unable to reconstruct shapes with boundary curves. We propose a hybrid shape representation that combines explicit boundary curves with implicit learned interiors. Using machinery from geometric measure theory, we parameterize currents using deep networks and
more » ... use stochastic gradient descent to solve a minimal surface problem. By modifying the metric according to target geometry coming, e.g., from a mesh or point cloud, we can use this approach to represent arbitrary surfaces, learning implicitly defined shapes with explicitly defined boundary curves. We further demonstrate learning families of shapes jointly parameterized by boundary curves and latent codes.
arXiv:2111.09383v2 fatcat:l7jv62o5jrevddktahtmph5n5e