Ing. et Ing. Václav Vávra

We propose a novel approach to estimate the fundamental matrix from point correspondences and their relative depths. Relative depths can be approximated from the scales of local features, which are commonly available or can be obtained from non-metric monocular depth estimates provided by popular deep learning-based methods. This makes the considered problem very relevant. To derive efficient solutions, we explore new geometric constraints on the fundamental matrix with known relative depths and present new algebraic constraints between the fundamental matrix and the translation vector. Using point correspondences and their relative depths, we derive novel efficient minimal solvers for two fully uncalibrated cameras, two cameras with different unknown focal lengths, and two cameras with equal unknown focal lengths, respectively. We propose different variants of these solvers based on the source of the relative depth information. We present detailed analyses and comparisons with state-of-the-art solvers, including results with 86, 306 image pairs from three large-scale datasets

We study the influence of image interpolation algorithms on local feature detectors operating on a scale pyramid, focusing on the Difference-of-Gaussian, as used in SIFT. We show that commonly used implementations, such as in OpenCV and Kornia, are neither rotational nor scale equivariant. We present a simple solution and demonstrate its positive influence on the downstream image matching tasks. The implementation of the method has been accepted in standard libraries OpenCV and Kornia.