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A coaxial camera rig consists of a pair of cameras which acquire images along the same optical axis but at different
distances from the scene using different focal length optics. The coaxial geometry permits the acquisition of image pairs
through a substantially smaller opening than would be required by a traditional binocular stereo camera rig. This is
advantageous in applications where physical space is limited, such as in an endoscope. 3D images acquired through an
endoscope are desirable, but the lack of physical space for a traditional stereo baseline is problematic. While image
acquisition along a common optical axis has been known for many years; 3D reconstruction from such image pairs has
not been possible in the center region due to the very small disparity between corresponding points. This characteristic of
coaxial image pairs has been called the unrecoverable point problem. We introduce a novel method to overcome the
unrecoverable point problem in coaxial camera rigs, using a variational methods optimization algorithm to map pairs of
optical flow fields from different focal length cameras in a coaxial camera rig. Our method uses the ratio of the optical
flow fields for 3D reconstruction. This results in accurate image pair alignment and produces accurate dense depth maps.
We test our method on synthetic optical flow fields and on real images. We demonstrate our method's accuracy by
evaluating against a ground-truth. Accuracy is comparable to a traditional binocular stereo camera rig, but without the
traditional stereo baseline and with substantially smaller occlusions.
Richard Kirby andRoss Whitaker
"3D reconstruction from images taken with a coaxial camera rig", Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 997106 (27 September 2016); https://doi.org/10.1117/12.2237172
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Richard Kirby, Ross Whitaker, "3D reconstruction from images taken with a coaxial camera rig," Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 997106 (27 September 2016); https://doi.org/10.1117/12.2237172