Building a database of 3D scenes from user annotations

Bryan Russell (Ecole Normale Supérieure, Paris, France)

In this paper, we wish to build a high quality database of images depicting scenes, along with their real-world three-dimensional (3D) coordinates. Such a database is useful for a variety of applications, including training systems for object detection and validation of 3D output. We build such a database from images that have been annotated with only the identity of objects and their spatial extent in images. Important for this task is the recovery of geometric information that is implicit in the object labels, such as qualtitative relationships between objects (attachment, support, occlusion) and quantitative ones (inferring camera parameters). We describe a model that integrates cues extracted from the object labels to infer the implicit geometric information. We show that we are able to obtain high quality 3D information by evaluating the proposed approach on a database obtained with a laser range scanner. Finally, given the database of 3D scenes, we show how it can find better scene matches for an unlabeled image by expanding the database through viewpoint interpolation to unseen views.