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Moreover, CNNs and Transformers struggle to capture global degradation due to their limited receptive fields.

This paper focuses on estimating probability distributions over the set of 3D rotations (SO(3)) using deep neural networks. Learning to regress models to the set of rotations is inherently difficult due to differences in topology between RN and SO(3). We overcome this issue by using a neural network to output the parameters for a matrix Fisher distribution since these parameters are homeomorphic toR9. By using a negative log likelihood loss for this distribution we get a loss which is convex with respect to the network outputs. By optimizing this loss we improve state-of-the-art on several challenging applicable datasets, namely Pascal3D+, ModelNet10-SO(3).

Nvidia CEO Jensen Huang said that a boom in artificial intelligence infrastructure will power the "largest infrastructure buildout in human history."