Pose Estimation for Two-View Panoramas based on Keypoint Matching: a Comparative Study and Critical Analysis

Pose estimation is a crucial problem in several computer vision and robotics applications. For the two-view scenario, the typical pipeline consists of finding point correspondences between the two views and using them to estimate the pose. However, most available keypoint extraction and matching methods were designed to work with perspective images and may fail under not-affine distortions present in wide-angle or omnidirectional media, which are becoming increasingly popular in recent years. This paper presents a comprehensive comparative analysis of different keypoint matching algorithms for panoramas coupled to different linear and non-linear approaches for pose estimation. As an additional contribution, we explore a recent approach for mitigating spherical distortions using tangent plane projections, which can be coupled with any planar descriptor, and allows the adaptation of recent learning-based methods. We evaluate the combination of keypoint matching and pose estimation methods using the rotation and translation error of the estimated pose in different scenarios (indoor and outdoor), and our results indicate that SPHORB and "tangent SIFT" are competitive algorithms. We also show that tangent plane adaptations frequently present competitive results, and some optimization steps consistently improve the performance in all methods. We provide code at https://github.com/Artcs1/Keypoints