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Pseudo-RGB-D for Self-Improving Monocular SLAM & Depth Prediction
Classical monocular simultaneous localization and mapping (SLAM) and the emerging convolutional neural networks (CNNs) for monocular depth prediction represent two largely disjoint approaches to building a 3D map of a surrounding environment. In this paper, we demonstrate that coupling these two approaches by leveraging the strengths of each mitigates the other’s shortcomings.
Collaborators: Manmohan Chandraker
