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   STEREO CORRESPONDENCE AND RECONSTRUCTION OF ENDOSCOPIC DATA SUPPORT
   
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STEREO CORRESPONDENCE AND RECONSTRUCTION OF ENDOSCOPIC DATA SUB-CHALLENGE


PART OF THE ENDOSCOPIC VISION CHALLENGE


This challenge will focus on depth estimation from endoscopic data. Depth
estimation and scene reconstruction are important components of many endoscopic
navigation and augmented reality guidance system. Endoscopic images can differ
considerably from natural scenes typically used in computer vision. In
particular, the harsh, directional lighting, smooth, non-planar surfaces and
subtle texture can pose challenges to traditional methods. Previous open
datasets for endoscopic data have used isolated ex-vivo or phantom organs with
registered models obtained from CT or structured light. However, these
techniques limit the depth estimation problem to artificially isolated organs
and do not provide direct measurement of the ground-truth stereo
correspondences. As a result, errors from registration, camera-calibration and
CT segmentation are not separated from the stereo-correspondence.

With this dataset, we hope to provide a similar baseline to the successful
Middlebury Stereo Dataset which has been successful in advancing the state of
the art in stereo methods over the last 2 decades.



PRIZES

Intuitive Surgical will be offering a prize of $4000 for the winner of this
challenge and $2000 for the runners up. 





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