High-quality Depth from Uncalibrated Small Motion Clip

Hyowon Ha        Sunghoon Im       Jaesik Park‡       Hae-Gon Jeon       In So Kweon

Korea Advanced Institute of Science and Technology (KAIST)      ‡Intel Labs

We 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.


High-quality Depth from Uncalibrated Small Motion Clip
Hyowon Ha, Sunghoon Im, Jaesik Park, Hae-Gon Jeon and In So Kweon
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2016
(Oral presentation)


[Google drive]

Source code & Dataset