Abstract

In this paper we study structure from motion problems for parallel cylinders. Using sparse keypoint correspondences is an efficient (and standard) way to solve the structure from motion problem. However, point features are sometimes unavailable and they can be unstable over time and viewing conditions. Instead, we propose a framework based on silhouettes of quadric surfaces, with special emphasis on parallel cylinders. Such structures are quite common, e.g. trees, lampposts, pillars, and furniture legs. Traditionally, the projection of the center lines of such cylinders have been considered and used in computer vision. Here, we demonstrate that the apparent width of the cylinders also contains useful information for structure and motion estimation. We provide mathematical analysis of relative structure and relative motion tensors, which is used to develop a number of minimal solvers for simultaneously estimating camera pose and scene structure from silhouette lines of cylinders. These solvers can be used efficiently in robust estimation schemes, such as RANSAC. We use Sampson-approximation methods for efficient estimation using over-determined data and develop averaging techniques. We also perform synthetic accuracy and robustness tests and evaluate our methods on a number of real-world scenarios.
Original languageEnglish
Title of host publicationImage Analysis
Subtitle of host publication23rd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings
EditorsRikke Gade, Michael Felsberg, Joni-Kristian Kämäräinen
PublisherSpringer
Pages482-499
ISBN (Electronic)978-3-031-31438-4
ISBN (Print)978-3-031-31437-7
DOIs
Publication statusPublished - 2023
Event22nd Scandinavian Conference on Image Analysis, SCIA 2023 - Sirkka, Finland
Duration: 2023 Apr 182023 Apr 21

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume13886
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd Scandinavian Conference on Image Analysis, SCIA 2023
Country/TerritoryFinland
CitySirkka
Period2023/04/182023/04/21

Subject classification (UKÄ)

  • Computer Vision and Robotics (Autonomous Systems)

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