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One major goal of vision is to infer physical models of objects, surfaces,and their layout from sensors. In this paper, we aim to interpret indoor scenesfrom one RGBD image. Our representation encodes the layout of walls, which mustconform to a Manhattan structure but is otherwise flexible, and the layout andextent of objects, modeled with CAD-like 3D shapes. We represent both thevisible and occluded portions of the scene, producing a complete 3D parse. Sucha scene interpretation is useful for robotics and visual reasoning, butdifficult to produce due to the well-known challenge of segmentation, the highdegree of occlusion, and the diversity of objects in indoor scene. We take adata-driven approach, generating sets of potential object regions, matching toregions in training images, and transferring and aligning associated 3D modelswhile encouraging fit to observations and overall consistency. We demonstrateencouraging results on the NYU v2 dataset and highlight a variety ofinteresting directions for future work.

Predicting Complete 3D Models of Indoor Scenes