Three-dimensional (3-D) models of outdoor scenes can be widely used in a number of fields such as object recognition, navigation, scenic simulation, and mixed reality. Such models are often made manually with high costs, so that automatic 3-D reconstruction has been widely investigated. In related works a dense 3-D model is generated by using a stereo method. However, such approaches cannot use several hundred images together for dense depth estimation of large constructs and urban environments because it is difficult to accurately calibrate a large number of cameras. This paper proposes a novel dense 3-D reconstruction method that uses multiple image sequences. First, our method estimates extrinsic camera parameters of each image sequence, and then reconstructs a dense 3-D model of a scene using an extended multi-baseline stereo and voxel voting techniques.
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