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Mid-level Likelihoods and Constraints for 3D Scene Interpretation

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How do you infer the 3D properties of the world from a single image? This question has eluded researchers in psychology and computer vision for decades, and enabling machines to accomplish this task remains an open question. In this talk, I will present my research towards solving the 3D interpretation task.

I will first talk about Data-Driven 3D Primitives, a new way of inferring surface normals / scene layout from a single image. These primitives are discovered from large-scale RGB -D data by optimizing two simple criteria: primitives should be visually discriminative and geometrically informative. I will show that a straightforward label-transfer inference approach on top of of these primitives produces state-of-the-art results on a complex and cluttered dataset, as well as effective cross-dataset performance.

Local cues, however, are inadequate by themselves: scenes are highly constrained in structure. I will therefore also talk about my work on constraining the 3D interpretation of scenes via physical and functional reasoning. Specifically, I will present work on mid-level physical constraints for layout estimation in the form of the convex and concave edges from the classic line-labeling literature. Finally, I will discuss how recognizing humans in scenes, even with imprecise and noisy pose estimators, can provide valuable cues for scene geometry via functional reasoning.

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