Post‐filtering with surface orientation constraints for stereo dense image matching
Abstract
Dense image matching (DIM) is a critical technique when computing accurate 3D geometric information for many photogrammetric applications. Most DIM methods adopt first‐order regularisation priors for efficient matching, which often introduce stepped biases (also called fronto‐parallel biases) into the matching results. To remove these biases and compute more accurate matching results, this paper proposes a novel post‐filtering method by adjusting the surface orientation of each pixel in the matching process. The core algorithm formulates the post‐filtering as the optimisation of a global energy function with second‐order regularisation priors. A compromise solution of the energy function is computed by breaking the optimisation into a collection of sub‐optimisations of each pixel in a local adaptive window. The proposed method was compared with several state‐of‐the‐art post‐filtering methods on indoor, aerial and satellite datasets. The comparisons demonstrate that the proposed method obtains the highest post‐filtering accuracies on all datasets.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 01, 2020
- Source ID
- 10.1111/phor.12333
Entities
People
- Rongjun Qin
- Xu Huang
Organizations
- Office of Naval Research
- Ohio State University