cvprtum's video: Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading

This is the video of the Spotlight presentation of our paper "Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading"; Bjoern Haefner, Yvain Quéau, Thomas Möllenhoff, Daniel Cremers; IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. Abstract: We put forward a principled variational approach for up-sampling a single depth map to the resolution of the companion color image provided by an RGB-D sensor. We combine heterogeneous depth and color data in order to jointly solve the ill-posed depth super-resolution and shape-from-shading problems. The low-frequency geometric information necessary to disambiguate shape-from-shading is extracted from the low-resolution depth measurements and, symmetrically, the high-resolution photometric clues in the RGB image provide the high-frequency information required to disambiguate depth super-resolution. PDF: https://vision.in.tum.de/_media/spezial/bib/haefner2018cvpr.pdf Supplementary material: https://vision.in.tum.de/_media/spezial/bib/haefner2018cvprsupp.pdf Code: https://github.com/BjoernHaefner/DepthSRfromShading CVF link: http://openaccess.thecvf.com/content_cvpr_2018/html/Haefner_Fight_Ill-Posedness_With_CVPR_2018_paper.html Bib entry: https://vision.in.tum.de/members/haefner?key=Haefner2018CVPR My chair web page: https://vision.in.tum.de/members/haefner Chair web page: https://vision.in.tum.de/