Authors:
Julian Bruno Seuffert
;
Ana Cecilia Perez Grassi
;
Tobias Scheck
and
Gangolf Hirtz
Affiliation:
Faculty of Electrical Engineering and Information Technology, Chemnitz University of Technology, Reichenhainer Str. 70, Chemnitz, Germany
Keyword(s):
Omnidirectional, Fish Eye, Indoor, 3D, CNN, Stereo Vision.
Abstract:
Stereo vision is one of the most prominent strategies to reconstruct a 3D scene with computer vision techniques. With the advent of Convolutional Neural Networks (CNN), stereo vision has undergone a breakthrough. Always more works attend to recover the depth information from stereo images by using CNNs. However, most of the existing approaches are developed for images captured with perspective cameras. Perspective cameras have a very limited field of view of around 60◦ and only a small portion of a scene can be reconstructed with a standard binocular stereo system. In the last decades, much effort has been conducted in the research field of omnidirectional stereo vision, which allows an almost complete scene reconstruction if the cameras are mounted at the ceiling. However, as omnidirectional images show strong distortion artifacts, most of the approaches perform an image warping to reduce the reconstruction complexity. In this work, we examine the impact of the omnidirectional image
distortion on the learning process of a CNN. We compare the results of a network training with perspective and omnidirectional stereo images. For this work, we use AnyNet and a novel dataset of synthetic omnidirectional and perspective stereo images.
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