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We integrate two powerful ideas, geometry and deep visual representationlearning, into recurrent network architectures for mobile visual sceneunderstanding. The proposed networks learn to "lift" and integrate 2D visualfeatures over time into latent 3D feature maps of the scene. They are equippedwith differentiable geometric operations, such as projection, unprojection,egomotion estimation and stabilization, in order to compute ageometrically-consistent mapping between the world scene and their 3D latentfeature state. We train the proposed architectures to predict novel cameraviews given short frame sequences as input. Their predictions stronglygeneralize to scenes with a novel number of objects, appearances andconfigurations; they greatly outperform previous works that do not consideregomotion stabilization or a space-aware latent feature state. We train theproposed architectures to detect and segment objects in 3D using the latent 3Dfeature map as input--as opposed to per frame features. The resulting objectdetections persist over time: they continue to exist even when an object getsoccluded or leaves the field of view. Our experiments suggest the proposedspace-aware latent feature memory and egomotion-stabilized convolutions areessential architectural choices for spatial common sense to emerge inartificial embodied visual agents.

Learning Spatial Common Sense with Geometry-Aware Recurrent Networks