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

ICLR 2020 Eric MitchellSelim EnginVolkan IslerDaniel D Lee

We present a new approach to 3D object representation where a neural network encodes the geometry of an object 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... (read more)

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