Unsupervised object segmentation for 2D to 3D conversion
Matthias Kunter, Sebastian B. Knorr, Andreas Krutz, Thomas Sikora · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
In this paper, we address the handling of independently moving objects (IMOs) in automatic 2D to stereoscopic 3D conversion systems based on structure-from-motion (SfM) techniques. Exploiting the different viewing positions of a moving camera, these techniques yield excellent 3D results for static scene objects. However, the independent motion of any foreground object requires a separate conversion process. We propose a novel segmentation approach that estimates the occluded static background and segments the IMOs based on advanced change detection. The background estimation is achieved applying 2D registration and blending techniques, representing an approximation of the underlying scene geometry. The segmentation process itself uses anisotropic filtering applied on the difference image between original frame and the estimated background frame. In order to render the segmented objects into the automatically generated 3D scene properly, a small amount of user interaction will be necessary, e.g. an assignment of intra-object depth or the object's absolute z-position. Experiments show that the segmentation method achieves accurate mask results for a variety of scenes, similar to the masks obtained manually using state-of-the-art rotoscoping tools. Though, this work contributes to the extension of SfM-based automatic 3D conversion methods for the application on dynamic scenes.