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Augmented Virtual Environments (AVE) are very effective in the application of surveillance, in which multiple video streams are projected onto a 3D urban model for better visualization and comprehension of the dynamic scenes. One of the key issues in creating such systems is to estimate the parameters of each camera including the intrinsic parameters and its pose relative to the 3D model. Existing camera pose estimation approaches require known intrinsic parameters and at least three 2D to 3Ddoi:10.1109/vr.2007.352494 dblp:conf/vr/WangYN07 fatcat:bp2rf7ltg5dp7kt3au3p2xrd4u