Dense 3-D reconstruction of an outdoor scene by hundreds-baseline stereo using a hand-held video camera
Three-dimensional (3-D) models of outdoor scenes are widely used for object recognition, navigation, mixed reality, and so on. Because such models are often made manually with high costs, automatic 3-D reconstruction has been widely investigated. In related work, a dense 3-D model is generated by using a stereo method. However, such approaches cannot use several hundreds images together for dense depth estimation because it is difficult to accurately calibrate a large number of cameras. In this paper, we propose a dense 3-D reconstruction method that first estimates extrinsic camera parameters of a hand-held video camera, and then reconstructs a dense 3-D model of a scene. In the first process, extrinsic camera parameters are estimated by tracking a small number of predefined markers of known 3-D positions and natural features automatically. Then, several hundreds dense depth maps obtained by multi-baseline stereo are combined together in a voxel space. So, we can acquire a dense 3-D model of the outdoor scene accurately by using several hundreds input images captured by a hand-held video camera.
marker natural feature multi-baseline stereo 3-d model reconstruction monocular image sequence