Combined Depth and Outlier Estimation in Multi-View Stereo
C. Strecha, R. Fransens, L. Van Gool
"Combined Depth and Outlier Estimation in Multi-View Stereo"
CVPR 2006[pdf]
abstract
In this paper, we present a generative model based approach to solve the multi-view stereo problem.
The input images are considered to be generated by either one of two processes: (i) an inlier process, which generates the pixels which are visible from the reference camera and which obey the constant brightness assumption, and (ii) an outlier process which generates all other pixels. Depth and visibility are jointly modelled as a hidden Markov Random Field, and the spatial correlations of both are explicitly accounted for. Inference is made tractable by an EM-algorithm, which alternates between estimation of visibility and depth, and optimisation of model parameters. We describe and compare two implementations of the E-step of the algorithm, which correspond to the Mean Field and Bethe approximations of the free energy. The approach is validated by experiments on challenging real-world scenes, of which two are contaminated by independently moving objects.
Brussels cityhall:
Input:3 images fully calibrated
Results:
visibility maps
depth and ideal image
Semper statue scene:
Input:3 images fully calibrated
Results:
visibility maps
depth and ideal image
results for templeRing scene [ply]
results for templeSparseRing scene [ply]
results for dinoRing scene [ply]
results for dinoSparseRing scene [ply]
C. Strecha, R. Fransens, L. Van Gool
"Combined Depth and Outlier Estimation in Multi-View Stereo"
CVPR 2006[pdf]