Robust Uncalibrated Stereo Rectification with Constrained Geometric Distortions (USR-CGD)
This addresses geometric distortion issues in stereo vision for applications like 3D reconstruction, though it appears incremental as it builds on existing rectification methods.
The paper tackles the problem of uncalibrated stereo image rectification by reducing geometric distortions while maintaining low rectification error, resulting in a method that outperforms existing algorithms by a significant margin.
A novel algorithm for uncalibrated stereo image-pair rectification under the constraint of geometric distortion, called USR-CGD, is presented in this work. Although it is straightforward to define a rectifying transformation (or homography) given the epipolar geometry, many existing algorithms have unwanted geometric distortions as a side effect. To obtain rectified images with reduced geometric distortions while maintaining a small rectification error, we parameterize the homography by considering the influence of various kinds of geometric distortions. Next, we define several geometric measures and incorporate them into a new cost function for parameter optimization. Finally, we propose a constrained adaptive optimization scheme to allow a balanced performance between the rectification error and the geometric error. Extensive experimental results are provided to demonstrate the superb performance of the proposed USR-CGD method, which outperforms existing algorithms by a significant margin.