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Recent advances in computer graphics and computer vision have foundsuccessful application of deep neural network models for 3D shapes based onsigned distance functions (SDFs) that are useful for shape representation,retrieval, and completion. However, this approach has been limited by the needto have query shapes in the same canonical scale and pose as those observedduring training, restricting its effectiveness on real world scenes. We presenta formulation to overcome this issue by jointly estimating shape and similaritytransform parameters. We conduct experiments to demonstrate the effectivenessof this formulation on synthetic and real datasets and report favorablecomparisons to the state of the art. Finally, we also emphasize the viabilityof this approach as a form of 3D model compression.

Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimation