Abstract:Rooms are important, because people live in rooms. Recent methods can now recover a reasonable, box-like approximate geometric model of a room from a single picture. This geometric model, while not necessarily exact, is surprisingly useful. For example, it can be used to enhance detection of furniture. I will show how relatively conventional detectors can be improved by taking this geometry into account. This box approximation can also be used to parse the room into occupied and free space. Free space is interesting because it has potential — free space consists of volumes into which one could move, for example.
However, free space is not empty. It is occupied by light traveling through the room. I will show how our box approximation allows us to extend relatively straightforward lightness and shading inference methods to produce relatively accurate estimates of illumination in space. These estimates can be used to
light new objects and insert them into the room. I will show numerous compelling examples of objects inserted into legacy images of rooms while preserving an appearance of natural lighting.
Finally, I show we can make estimates of the parameters (diffuse albedo, etc.) of the materials from which objects are made. This works because we know how much light is where in a room, and because we can see the light leaving objects.