ESC: Evolutionary Stitched Camera Calibration in the Wild

• URL: http://arxiv.org/abs/2404.12694v1
• Date: Fri, 19 Apr 2024 07:50:13 GMT
• Title: ESC: Evolutionary Stitched Camera Calibration in the Wild
• Authors: Grzegorz Rypeść, Grzegorz Kurzejamski,
• Abstract summary: We identify the source of significant calibration errors in multi-camera environments. We propose the Evolutionary Stitched Camera calibration algorithm to bridge this gap. We demonstrate the superior performance of our approach compared to state-of-the-art methods across diverse real-life football fields.
• Score: 0.15346678870160887
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This work introduces a novel end-to-end approach for estimating extrinsic parameters of cameras in multi-camera setups on real-life sports fields. We identify the source of significant calibration errors in multi-camera environments and address the limitations of existing calibration methods, particularly the disparity between theoretical models and actual sports field characteristics. We propose the Evolutionary Stitched Camera calibration (ESC) algorithm to bridge this gap. It consists of image segmentation followed by evolutionary optimization of a novel loss function, providing a unified and accurate multi-camera calibration solution with high visual fidelity. The outcome allows the creation of virtual stitched views from multiple video sources, being as important for practical applications as numerical accuracy. We demonstrate the superior performance of our approach compared to state-of-the-art methods across diverse real-life football fields with varying physical characteristics.

Related papers

• Redundancy-Aware Camera Selection for Indoor Scene Neural Rendering [54.468355408388675]
  We build a similarity matrix that incorporates both the spatial diversity of the cameras and the semantic variation of the images. We apply a diversity-based sampling algorithm to optimize the camera selection. We also develop a new dataset, IndoorTraj, which includes long and complex camera movements captured by humans in virtual indoor environments.
  arXiv  Detail & Related papers  (2024-09-11T08:36:49Z)
• PnLCalib: Sports Field Registration via Points and Lines Optimization [16.278222277579655]
  Camera calibration in broadcast sports videos presents numerous challenges for accurate sports field registration. Traditional search-based methods depend on initial camera pose estimates, which can struggle in non-standard positions. We propose an optimization-based calibration pipeline that leverages a 3D soccer field model and a predefined set of keypoints to overcome these limitations.
  arXiv  Detail & Related papers  (2024-04-12T11:15:15Z)
• Toward Efficient Visual Gyroscopes: Spherical Moments, Harmonics Filtering, and Masking Techniques for Spherical Camera Applications [83.8743080143778]
  A visual gyroscope estimates camera rotation through images. The integration of omnidirectional cameras, offering a larger field of view compared to traditional RGB cameras, has proven to yield more accurate and robust results. Here, we address these challenges by introducing a novel visual gyroscope, which combines an Efficient Multi-Mask-Filter Rotation Estor and a Learning based optimization.
  arXiv  Detail & Related papers  (2024-04-02T13:19:06Z)
• VICAN: Very Efficient Calibration Algorithm for Large Camera Networks [49.17165360280794]
  We introduce a novel methodology that extends Pose Graph Optimization techniques. We consider the bipartite graph encompassing cameras, object poses evolving dynamically, and camera-object relative transformations at each time step. Our framework retains compatibility with traditional PGO solvers, but its efficacy benefits from a custom-tailored optimization scheme.
  arXiv  Detail & Related papers  (2024-03-25T17:47:03Z)
• Single-image camera calibration with model-free distortion correction [0.0]
  This paper proposes a method for estimating the complete set of calibration parameters from a single image of a planar speckle pattern covering the entire sensor. The correspondence between image points and physical points on the calibration target is obtained using Digital Image Correlation. At the end of the procedure, a dense and uniform model-free distortion map is obtained over the entire image.
  arXiv  Detail & Related papers  (2024-03-02T16:51:35Z)
• E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras [34.71767308204867]
  We present E-Calib, a novel, fast, robust, and accurate calibration toolbox for event cameras. The proposed method is tested in a variety of rigorous experiments for different event camera models.
  arXiv  Detail & Related papers  (2023-06-15T12:16:38Z)
• Online Camera-to-ground Calibration for Autonomous Driving [26.357898919134833]
  We propose an online monocular camera-to-ground calibration solution that does not utilize any specific targets while driving. We provide metrics to quantify calibration performance and stopping criteria to report/broadcast our satisfying calibration results.
  arXiv  Detail & Related papers  (2023-03-30T04:01:48Z)
• Self-Supervised Camera Self-Calibration from Video [34.35533943247917]
  We propose a learning algorithm to regress per-sequence calibration parameters using an efficient family of general camera models. Our procedure achieves self-calibration results with sub-pixel reprojection error, outperforming other learning-based methods.
  arXiv  Detail & Related papers  (2021-12-06T19:42:05Z)
• DeepMultiCap: Performance Capture of Multiple Characters Using Sparse Multiview Cameras [63.186486240525554]
  DeepMultiCap is a novel method for multi-person performance capture using sparse multi-view cameras. Our method can capture time varying surface details without the need of using pre-scanned template models.
  arXiv  Detail & Related papers  (2021-05-01T14:32:13Z)
• Wide-angle Image Rectification: A Survey [86.36118799330802]
  wide-angle images contain distortions that violate the assumptions underlying pinhole camera models. Image rectification, which aims to correct these distortions, can solve these problems. We present a detailed description and discussion of the camera models used in different approaches. Next, we review both traditional geometry-based image rectification methods and deep learning-based methods.
  arXiv  Detail & Related papers  (2020-10-30T17:28:40Z)
• Redesigning SLAM for Arbitrary Multi-Camera Systems [51.81798192085111]
  Adding more cameras to SLAM systems improves robustness and accuracy but complicates the design of the visual front-end significantly. In this work, we aim at an adaptive SLAM system that works for arbitrary multi-camera setups. We adapt a state-of-the-art visual-inertial odometry with these modifications, and experimental results show that the modified pipeline can adapt to a wide range of camera setups.
  arXiv  Detail & Related papers  (2020-03-04T11:44:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.