Geometry-based regularisation for dense image matching via uncertainty-driven depth propagation
In ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (ISPRS-2020), Nice, Copernicus Publications, 2020.
In the present work, an uncertainty-driven geometry-based regularisation for the task of dense stereo matching is presented. The
objective of the regularisation is the reduction of ambiguities in the depth reconstruction process, which exist due to the ill-posed
nature of this task. Based on cost and uncertainty information computed beforehand, pixels are selected, whose depth information
can be determined correctly with a high probability. This depth information assumed to be of high confidence is initially used to
construct a triangle mesh, which is interpreted as surface approximation of the imaged scene and allows to propagate the confident
depth information of the triangle vertices within local neighbourhoods. The proposed method further computes confidence scores
for propagated depth estimates, which are used to fuse this depth information with the previously computed cost information, introducing a regularisation into the data term of global optimisation methods. Furthermore, based on the propagated depth information
the local smoothness assumption of global optimisation methods is adjusted. Instead of fronto-parallel planes, the method presumes
planes, which are parallel to the propagated depth information. The performance of the proposed regularisation approach is evaluated in combination with a global optimisation method. For a quantitative and qualitative evaluation two commonly employed and
well-established stereo datasets are used. The proposed method shows significant improvements in accuracy on both datasets and
for two different cost computation methods. Especially in unstructured areas, artefacts in the disparity maps are reduced.
Dense Image Matching, Depth Reconstruction, Regularisation, Triangle Mesh, Confidence