3 accepted papers
We integrate two powerful ideas, geometry and deep visual representation learning, into recurrent network architectures for mobile visual scene understanding. The proposed networks learn to "lift" 2D visual features and integrate them over time into latent 3D feature maps of the scene. They are equi…
We present recurrent geometry-aware neural networks that integrate visual in- formation across multiple views of a scene into 3D latent feature tensors, while maintaining an one-to-one mapping between 3D physical locations in the world scene and latent feature locations. Object detection, object seg…
We consider artificial agents that learn to jointly control their gripper and camera in order to reinforcement learn manipulation policies in the presence of occlusions from distractor objects. Distractors often occlude the object of interest and cause it to disappear from the field of view. We prop