Neural networks estimate depth distributions from event camera data using six input representations and three uncertainty models, with 10-bin log-normal and 5-bin evidential variants performing best on synthetic-to-real transfer.
Combining events and frames using recurrent asynchronous multimodal networks for monocular depth prediction
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Neuromorphic Monocular Depth Estimation with Uncertainty Modeling
Neural networks estimate depth distributions from event camera data using six input representations and three uncertainty models, with 10-bin log-normal and 5-bin evidential variants performing best on synthetic-to-real transfer.