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

Recent advances in monocular depth estimation methods (MDEs) and their improved accuracy open new possibilities for their applications. In this paper, we investigate how monocular depth estimates can be used for relative pose estimation. In particular, we are interested in answering the question whether using MDEs improves results over traditional point-based methods. We propose a novel framework for estimating the relative pose of two cameras from point correspondences with associated monocular depths. Since depth predictions are typically defined up to an unknown scale or even both unknown scale and shift parameters, our solvers jointly estimate the scale or both the scale and shift parameters along with the relative pose. We derive efficient solvers considering different types of depths for three camera configurations: (1) two calibrated cameras, (2) two cameras with an unknown shared focal length, and (3) two cameras with unknown different focal lengths. Our new solvers outperform stateof-the-art depth-aware solvers in terms of speed and accuracy. In extensive real experiments on multiple datasets and with various MDEs, we discuss which depth-aware solvers are preferable in which situation. The code is available at https://github.com/kocurvik/mdrp.

Visual localization is an important part of many interesting applications, including robotics. The dominant localization strategy is to estimate the camera pose from 2D-3D matches between 2D pixel positions and 3D points. Yet, such approaches can be quite memory intensive and can lead to privacy risks. An interesting alternative to point-based matches is to use higher-level primitives for pose estimation. Consequently, this work investigates using correspondences between 2D and 3D bounding boxes for camera pose estimation. The resulting scene representation is compact and poses fewer privacy risks. In this setting, there are typically orders of magnitude fewer matches available compared to classical feature-based methods. In addition, the available correspondences are significantly more noisy. We investigate multiple strategies based on converting bounding box correspondences to point correspondences and propose a novel and simple 2-point camera absolute pose solver (DP2P) that exploits the fact that the depths of the objects can be approximated from the sizes of their bounding boxes.

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.