Higher-Order Function Networks for Learning Composable 3D Object Representations

Sep 25, 2019 Blind Submission readers: everyone Show Bibtex
  • TL;DR: Neural nets can encode complex 3D objects into the parameters of other (surprisingly small) neural nets
  • Abstract: We present a new approach to 3D object representation where the geometry of an object is encoded directly into the weights and biases of a second 'mapping' network. This mapping network can be used to reconstruct an object by applying its encoded transformation to points randomly sampled from a simple geometric space, such as the unit sphere. Next, we extend this concept to enable the composition of multiple mapping functions. This capability provides a method for mixing features of different objects through function composition in a latent function space. Our experiments examine the effectiveness of our method on a subset of the ShapeNet dataset. We find that this representation can reconstruct objects with accuracy equal to or exceeding state-of-the-art methods with orders of magnitude fewer parameters. Our smallest reconstruction network has only about 7000 parameters and shows reconstruction quality on par with state-of-the-art object representation architectures with millions of parameters.
  • Keywords: computer vision, 3d reconstruction, deep learning, representation learning
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