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This paper presents a method of estimating the geometry of a room and the 3Dpose of objects from a single 360-degree panorama image. Assuming ManhattanWorld geometry, we formulate the task as a Bayesian inference problem in whichwe estimate positions and orientations of walls and objects. The methodcombines surface normal estimation, 2D object detection and 3D object poseestimation. Quantitative results are presented on a dataset of syntheticallygenerated 3D rooms containing objects, as well as on a subset of hand-labeledimages from the public SUN360 dataset.

Pano2CAD: Room Layout From A Single Panorama Image