Indoor Scene Classi cation |
Scene categorization is an important mechanism for providing high-level context which can guide methods for a more detailed analysis of scenes. State-of-the-art techniques like Torralba's Gist features show a good performance on categorizing outdoor scenes but have problems in categorizing indoor scenes. In contrast to object based approaches, we propose a 3D feature vector capturing general properties of the spatial layout of indoor scenes like shape and size of extracted planar patches and their orientation to each other. This idea is supported by psychological experiments which give evidence for the special role of 3D geometry in categorizing indoor scenes. In order to study the influence of the 3D geometry we introduce in this paper a novel 3D indoor database and a method for defining 3D features on planar surfaces extracted in 3D data. Additionally, we propose a voting technique to fuse 3D features and 2D Gist features and show in our experiments a significant contribution of the 3D features to the indoor scene categorization task.
The 3D-IKEA database can be downloaded as archive. The archive contains 28 rooms of 6 room types with 300 to 400 frames per room recorded with SwissRanger camera being panned and tilted continuously. Some videos give an impression of the recorded data. Also some matlab code is provided to load, preprocess, and visualize the data. Further, methods are available for extracting planar patches in a frame and transforming them to a 3D spatial feature vector used for scene classification. [3D-IKEA-database.tar.gz]
