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We study the notion of consistency between a 3D shape and a 2D observation and propose a differentiable formulation which allows computing gradients of the 3D shape given an observation from an arbitrary view. We do so by reformulating view consistency using a differentiable ray consistency (DRC) term. We show that this formulation can be incorporated in a learning framework to leverage different...
We address the problem of fully automatic object localization and reconstruction from a single image. This is both a very challenging and very important problem which has, until recently, received limited attention due to difficulties in segmenting objects and predicting their poses. Here we leverage recent advances in learning convolutional networks for object detection and segmentation and introduce...
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