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Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene
[article]
2018
arXiv
pre-print
The goal of this paper is to take a single 2D image of a scene and recover the 3D structure in terms of a small set of factors: a layout representing the enclosing surfaces as well as a set of objects represented in terms of shape and pose. We propose a convolutional neural network-based approach to predict this representation and benchmark it on a large dataset of indoor scenes. Our experiments evaluate a number of practical design questions, demonstrate that we can infer this representation,
arXiv:1712.01812v2
fatcat:cwpfek42s5bvzdvkh5iciy6gk4