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We present a variational segmentation method which exploits color, edge and spatial information between an arbitrary number of views. In contrast to purely image based information like color and gradient, spatial consistency is a new cue for segmentation, which originates from the field of 3D reconstruction. We show that this cue can be easily integrated in a variational formulation and allows pixel-accurate segmentation, even for objects which are hard to segment. The use of inherently parallel algorithms and the implementation on modern GPUs allows us to apply this method to semi-supervised and completely automatic settings. On publicly available datasets we show that our method is faster and more accurate than the state of the art. The successful applications within a catadioptric measurement system and multi-view background subtraction shows its practical relevance.