CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2004 vol.26 Issue No.07 - July
Issue No.07 - July (2004 vol.26)
Hong Hua , IEEE Computer Society
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2004.33
<p><b>Abstract</b>—A mirror pyramid consists of a set of planar mirror faces arranged around an axis of symmetry and inclined to form a pyramid. By strategically positioning a number of conventional cameras around a mirror pyramid, the viewpoints of the cameras' mirror images can be located at a single point within the pyramid and their optical axes pointed in different directions to effectively form a virtual camera with a panoramic field of view. Mirror pyramid-based panoramic cameras have a number of attractive properties, including single-viewpoint imaging, high resolution, and video rate capture. It is also possible to place multiple viewpoints within a single mirror pyramid, yielding compact designs for simultaneous multiview panoramic video rate imaging. Nalwa  first described some of the basic ideas behind mirror pyramid cameras. In this paper, we analyze the general class of multiview panoramic cameras, provide a method for designing these cameras, and present experimental results using a prototype we have developed to validate single-pyramid multiview designs. We first give a description of mirror pyramid cameras, including the imaging geometry, and investigate the relationship between the placement of viewpoints within the pyramid and the cameras' field of view (FOV), using simulations to illustrate the concepts. A method for maximizing sensor utilization in a mirror pyramid-based multiview panoramic camera is also presented. Images acquired using the experimental prototype for two viewpoints are shown.</p>
Panoramic cameras, mirror pyramids, catadioptric systems, omnidirectional imaging and video capture, multiview panoramic imaging, stereoscopic cameras.
Hong Hua, Kar-Han Tan, "Multiview Panoramic Cameras Using Mirror Pyramids", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.26, no. 7, pp. 941-946, July 2004, doi:10.1109/TPAMI.2004.33