A Detailed Analysis of a New 3D Spatial Feature Vector for Indoor Scene Classification

Swadzba A, Wachsmuth S (2014)
Robotics and Autonomous Systems 62(5): 646-662.

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Journal Article | Published | English
Abstract
Enhancing perception of the local environment with semantic information like the room type is an important ability for agents acting in their environment. Such high-level knowledge can reduce the effort needed for, for example, object detection. This paper shows how to extract the room label from a small amount of room percepts taken from a certain view point (like the door frame when entering the room). Such functionality is similar to the human ability to get a scene impression from a quick glance. We propose a new three-dimensional (3D) spatial feature vector that captures the layout of a scene from extracted planar surfaces. The trained models emulate the human brain sensitivity to the 3D geometry of a room. Further, we show that our descriptor complements the information encoded by the Gist feature vector — a first attempt to model the mentioned brain area. The global scene properties are extracted from edge information in 2D depictions of the scene. Both features can be fused, resulting in a system that follows our goal to combine psychological insights on human scene perception with physical properties of environments. This paper provides detailed insights into the nature of our spatial descriptor.
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Swadzba A, Wachsmuth S. A Detailed Analysis of a New 3D Spatial Feature Vector for Indoor Scene Classification. Robotics and Autonomous Systems. 2014;62(5):646-662.
Swadzba, A., & Wachsmuth, S. (2014). A Detailed Analysis of a New 3D Spatial Feature Vector for Indoor Scene Classification. Robotics and Autonomous Systems, 62(5), 646-662.
Swadzba, A., and Wachsmuth, S. (2014). A Detailed Analysis of a New 3D Spatial Feature Vector for Indoor Scene Classification. Robotics and Autonomous Systems 62, 646-662.
Swadzba, A., & Wachsmuth, S., 2014. A Detailed Analysis of a New 3D Spatial Feature Vector for Indoor Scene Classification. Robotics and Autonomous Systems, 62(5), p 646-662.
A. Swadzba and S. Wachsmuth, “A Detailed Analysis of a New 3D Spatial Feature Vector for Indoor Scene Classification”, Robotics and Autonomous Systems, vol. 62, 2014, pp. 646-662.
Swadzba, A., Wachsmuth, S.: A Detailed Analysis of a New 3D Spatial Feature Vector for Indoor Scene Classification. Robotics and Autonomous Systems. 62, 646-662 (2014).
Swadzba, Agnes, and Wachsmuth, Sven. “A Detailed Analysis of a New 3D Spatial Feature Vector for Indoor Scene Classification”. Robotics and Autonomous Systems 62.5 (2014): 646-662.
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2639459
3D Indoor Scenes Database
Swadzba A, Wachsmuth S (2009)
Bielefeld University.
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