Calibrated and Partially Calibrated Semi-Generalized Homographies

In this paper, we propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera. The proposed solvers use five 2D-2D image point correspondences induced by a scene plane. One of them assumes the perspective camera to be fully calibrated, while the other solver estimates the unknown focal length together with the absolute pose parameters. This setup is particularly important in structure-from-motion and image-based localization pipelines, where a new camera is localized in each step with respect to a set of known cameras and 2D-3D correspondences might not be available. As a consequence of a clever parametrization and the elimination ideal method, our approach only needs to solve a univariate polynomial of degree five or three. The proposed solvers are stable and efficient as demonstrated by a number of synthetic and real-world experiments

Robust semi-generalized camera pose estimation using multiple minimal solvers

Camera pose estimation is a fundamental problem in computer vision with various ap- plications, such as in Structure-from-Motion and Visual Localization. These applications often focus on registering a new camera to a set of already registered cameras with known poses. Thus, camera poses can be estimated by solving the semi-generalized relative pose problem. This is usually achieved by using a minimal solver inside a RANSAC-style robust estimator. This estimator can handle data with a large amount of outliers. While solvers for the semi-generalized relative pose problem are typically too inefficient for practical use, the highly related semi-generalized homography problem can be solved effi- ciently. This thesis develops and investigates different implementations, e.g., using Sturm sequences or companion matrix approaches, for multiple solvers for this problem. This thesis further proposes a novel RANSAC-based approach that automatically selects the most suitable solver for each iteration in a data-driven way. In detailed experiments, the solvers are evaluated in terms of efficiency and stability. Extensive experiments show the effectiveness of our novel RANSAC-based approach by comparing it against two baselines.

Robustní odhad pozice částečně obecné kamery za použití několika minimálních řešičů

Camera pose estimation is a fundamental problem in computer vision with various ap- plications, such as in Structure-from-Motion and Visual Localization. These applications often focus on registering a new camera to a set of already registered cameras with known poses. Thus, camera poses can be estimated by solving the semi-generalized relative pose problem. This is usually achieved by using a minimal solver inside a RANSAC-style robust estimator. This estimator can handle data with a large amount of outliers. While solvers for the semi-generalized relative pose problem are typically too inefficient for practical use, the highly related semi-generalized homography problem can be solved effi- ciently. This thesis develops and investigates different implementations, e.g., using Sturm sequences or companion matrix approaches, for multiple solvers for this problem. This thesis further proposes a novel RANSAC-based approach that automatically selects the most suitable solver for each iteration in a data-driven way. In detailed experiments, the solvers are evaluated in terms of efficiency and stability. Extensive experiments show the effectiveness of our novel RANSAC-based approach by comparing it against two baselines. 1Odhad pozície kamery je základným problémom počítačového videnia s rôznymi apli- káciami, ako napríklad "Structure-from-Motion" (Štruktúra z pohybu) a "Visual Locali- zation" (Vizuálna lokalizácia). Tieto aplikácie sa často zameriavajú na registráciu novej kamery voči množine už zaregistrovaných kamier so známymi pozíciami. Polohy kamery je teda možné odhadnúť vyriešením "semi-generalized relative pose problem" (problému čiastočne zovšeobecnenej relatívnej pozície). To sa zvyčajne dosiahne použitím minimál- neho riešiča (minimal solver) vo vnútri robustného estimátora v štýle RANSAC. Tento es- timátor zvládne spracovať údaje s veľkým množstvom odľahlých hodnôt (outliers). Zatiaľ čo riešiče čiastočne zovšeobecného problému relatívnej pozície sú zvyčajne príliš neefek- tívne na praktické použitie, veľmi súvisiaci problém čiastočne zovšeobecnenej homografie možno efektívne vyriešiť. Táto práca vyvíja a skúma rôzne implementácie pre viacero riešičov tohto problému, napr. pomocou Sturmových sekvencií alebo pomocou matice pridruženej polynómu (companion matrix). Táto práca ďalej navrhuje nový prístup za- ložený na RANSAC, ktorý automaticky vyberie najvhodnejší riešič pre každú iteráciu na základe dát. V podrobných experimentoch sú riešiče hodnotené z hľadiska efektivity a stability. Rozsiahle experimenty ukazujú...Department of Software EngineeringKatedra softwarového inženýrstvíFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult