Depth Information by Stage Classification

Recently, methods for estimating 3D scene geometry or absolute scene depth information from 2D image content have been proposed. However, general applicability of these methods in depth estimation may not be realizable, as inconsistencies may be introduced due to a large variety of possible pictorial content. We identify scene categorization as the first step towards efficient and robust depth estimation from single images. To that end, we describe a limited number of typical 3D scene geometries, called stages, each having a unique depth pattern and thus providing a specific context for stage objects. This type of scene information narrows down the possibilities with respect to individual objects' locations, scales and identities. We show how these stage types can be efficiently learned and how they can lead to robust extraction of depth information. Our results indicate that stages without much variation and object clutter can be detected robustly, with up to 60% success rate.