We present a common variational framework for dense depth recovery and dense three-dimensional motion field estimation from multiple video sequences, which is robust to camera spe...
Jean-Philippe Pons, Renaud Keriven, Olivier D. Fau...
We propose a graph-based semi-supervised symmetric matching framework that performs dense matching between two uncalibrated wide-baseline images by exploiting the results of sparse...
Jianxiong Xiao, Jingni Chen, Dit-Yan Yeung, Long Q...
This paper proposes a method for computing a quasi-dense set of matching points between three views of a scene. The method takes a sparse set of seed matches between pairs of view...
Pekka Koskenkorva, Juho Kannala, Sami Sebastian Br...
We propose a novel energy minimisation framework for the dense reconstruction of stereo image sequences that incorporates data fidelity as well as spatial and temporal regularity....
Ben Appleton, Brian C. Lovell, Carlos Leung, Chang...
Dense depth maps can be estimated in a Bayesian sense from multiple calibrated still images of a rigid scene relative to a reference view [1]. This well-established probabilistic f...
Peter Wey, Bernd Fischer, Herbert Bay, Joachim M. ...