The issue of the automatic reconstruction of 3D scenes has been addressed in several chapters over the last few years. Many of them describe techniques for processing stereo vision or range images captured by high quality range sensors. However, due to the high price of such input devices, most of the methods proposed in the literature are not suitable for real-world scenarios. This chapter proposes a method designed to reconstruct 3D scenes perceived by means of a cheap device, namely the Kinect sensor. The scene is efficiently represented as a composition of superquadric shapes so as to obtain a compact description of environment, however complex it may be. The approach proposed here is intended to be used as a novel processing module of a well-established cognitive architecture for artificial vision. Experimental tests have been performed on real images and the results look very promising.
Morana, M. (2014). 3D Scene Reconstruction Using Kinect. In Advances onto the Internet of Things How Ontologies Make the Internet of Things Meaningful [10.1007/978-3-319-03992-3_13].
3D Scene Reconstruction Using Kinect
MORANA, Marco
2014-01-01
Abstract
The issue of the automatic reconstruction of 3D scenes has been addressed in several chapters over the last few years. Many of them describe techniques for processing stereo vision or range images captured by high quality range sensors. However, due to the high price of such input devices, most of the methods proposed in the literature are not suitable for real-world scenarios. This chapter proposes a method designed to reconstruct 3D scenes perceived by means of a cheap device, namely the Kinect sensor. The scene is efficiently represented as a composition of superquadric shapes so as to obtain a compact description of environment, however complex it may be. The approach proposed here is intended to be used as a novel processing module of a well-established cognitive architecture for artificial vision. Experimental tests have been performed on real images and the results look very promising.File | Dimensione | Formato | |
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