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Three-dimensional (3D) 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 cost, so that automatic 3D reconstruction has been widely investigated. In related works, a dense 3D model is generated by using a stereo method. However, such approaches cannot use several hundred images together or 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 dense 3D reconstruction method that uses multiple image sequences. First, our method estimates extrinsic camera parameters of each image sequence, then reconstructs a dense 3D model of a scene using an extended multi-baseline stereo and voxel voting technique.