We present an image-based rendering system to viewpoint-navigate through space and time of complex real-world, dynamic scenes. Our approach accepts unsynchronized, uncalibrated multivideo footage as input. Inexpensive, consumer-grade camcorders suffice to acquire arbitrary scenes, for example in the outdoors, without elaborate recording setup procedures, allowing also for hand-held recordings. Instead of scene depth estimation, layer segmentation or 3D reconstruction, our approach is based on dense image correspondences, treating view interpolation uniformly in space and time: spatial viewpoint navigation, slow motion or freeze-and-rotate effects can all be created in the same way. Acquisition simplification, integration of moving cameras, generalization to difficult scenes and space-time symmetric interpolation amount to a widely applicable virtual video camera system.

The 2007 DARPA Urban Challenge afforded the golden opportunity for the Technische Universität Braunschweig to demonstrate its abilities to develop an autonomously driving vehicle to compete with the world's best. After several stages of qualification, our team CarOLO qualified early for the DARPA Urban Challenge Final Event and was among only 11 teams from initially 89 competitors to compete in the final. We had the ability to work together in a large group of experts, each contributing his expertise in his discipline, and significant organizational, financial, and technical support by local sponsors, who helped us to become the best non‐U.S. team. In this report, we describe the 2007 DARPA Urban Challenge, our contribution, “Caroline,” the technology, and algorithms, along with her performance in the DARPA Urban Challenge Final Event on November 3, 2007. © 2008 Wiley Periodicals, Inc.

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We present a lane detection algorithm that robustly detects

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