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Monocular depth inference has gained tremendous attention from researchers inrecent years and remains as a promising replacement for expensivetime-of-flight sensors, but issues with scale acquisition and implementationoverhead still plague these systems. To this end, this work presents anunsupervised learning framework that is able to predict at-scale depth maps andegomotion, in addition to camera intrinsics, from a sequence of monocularimages via a single network. Our method incorporates both spatial and temporalgeometric constraints to resolve depth and pose scale factors, which areenforced within the supervisory reconstruction loss functions at training time.Only unlabeled stereo sequences are required for training the weights of oursingle-network architecture, which reduces overall implementation overhead ascompared to previous methods. Our results demonstrate strong performance whencompared to the current state-of-the-art on multiple sequences of the KITTIdriving dataset and can provide faster training times with its reduced networkcomplexity.

Unsupervised Monocular Depth Learning with Integrated Intrinsics and Spatio-Temporal Constraints