https://sites.google.com/site/hyowoncv/ha_cvpr16We propose a novel approach that generates a high-quality depth map from a set of images captured with a small viewpoint variation, namely small motion clip. As opposed to prior methods that recover scene geometry and camera motions using pre-calibrated cameras, we introduce a self-calibrating bundle adjustment tailored for small motion. This allows our dense stereo algorithm to produce a high-quality depth map for the user without the need for camera calibration. In the dense matching, the distributions of intensity profiles are analyzed to leverage the benefit of having negligible intensity changes within the scene due to the minuscule variation in viewpoint. The depth maps obtained by the proposed framework show accurate and extremely fine structures that are unmatched by previous literature under the same small motion configuration.
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|Hyowon Ha, Sunghoon Im, Jaesik Park, Hae-Gon Jeon, In So Kweon
|IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [oral]
|This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No.2010-0028680).