Calibration Venus: An Interactive Camera Calibration Method Based on Search Algorithm and Pose Decomposition

• URL: http://arxiv.org/abs/2009.05983v1
• Date: Sun, 13 Sep 2020 12:12:10 GMT
• Title: Calibration Venus: An Interactive Camera Calibration Method Based on Search Algorithm and Pose Decomposition
• Authors: Wentai Lei, Mengdi Xu, Feifei Hou, Wensi Jiang
• Abstract summary: The interactive calibration method based on the plane board is becoming popular in camera calibration field due to its repeatability and operation advantages. The existing methods select suggestions from a fixed dataset of pre-defined poses based on subjective experience, which leads to a certain degree of one-sidedness.
• Score: 2.878441608970396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In many scenarios where cameras are applied, such as robot positioning and unmanned driving, camera calibration is one of the most important pre-work. The interactive calibration method based on the plane board is becoming popular in camera calibration field due to its repeatability and operation advantages. However, the existing methods select suggestions from a fixed dataset of pre-defined poses based on subjective experience, which leads to a certain degree of one-sidedness. Moreover, they does not give users clear instructions on how to place the board in the specified pose.

Related papers

• PuzzleBoard: A New Camera Calibration Pattern with Position Encoding [0.0]
  We present a new calibration pattern that combines the advantages of checkerboard calibration patterns with a lightweight position coding. The whole approach is backward compatible to both checkerboard calibration patterns and several checkerboard calibration algorithms.
  arXiv  Detail & Related papers  (2024-09-30T09:27:06Z)
• EasyHeC: Accurate and Automatic Hand-eye Calibration via Differentiable Rendering and Space Exploration [49.90228618894857]
  We introduce a new approach to hand-eye calibration called EasyHeC, which is markerless, white-box, and delivers superior accuracy and robustness. We propose to use two key technologies: differentiable rendering-based camera pose optimization and consistency-based joint space exploration. Our evaluation demonstrates superior performance in synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-05-02T03:49:54Z)
• Deep Learning for Camera Calibration and Beyond: A Survey [100.75060862015945]
  Camera calibration involves estimating camera parameters to infer geometric features from captured sequences. Recent efforts show that learning-based solutions have the potential to be used in place of the repeatability works of manual calibrations.
  arXiv  Detail & Related papers  (2023-03-19T04:00:05Z)
• Online Marker-free Extrinsic Camera Calibration using Person Keypoint Detections [25.393382192511716]
  We propose a marker-free online method for the extrinsic calibration of multiple smart edge sensors. Our method assumes the intrinsic camera parameters to be known and requires priming with a rough initial estimate of the camera poses. We show that the calibration with our method achieves lower reprojection errors compared to a reference calibration generated by an offline method.
  arXiv  Detail & Related papers  (2022-09-15T15:54:21Z)
• How to Calibrate Your Event Camera [58.80418612800161]
  We propose a generic event camera calibration framework using image reconstruction. We show that neural-network-based image reconstruction is well suited for the task of intrinsic and extrinsic calibration of event cameras.
  arXiv  Detail & Related papers  (2021-05-26T07:06:58Z)
• Calibrated and Partially Calibrated Semi-Generalized Homographies [65.29477277713205]
  We propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera. The proposed solvers are stable and efficient as demonstrated by a number of synthetic and real-world experiments.
  arXiv  Detail & Related papers  (2021-03-11T08:56:24Z)
• Procam Calibration from a Single Pose of a Planar Target [0.30458514384586405]
  A novel user friendly method is proposed for calibrating a procam system from a single pose of a planar chessboard target. A sequence of Gray Code patterns are projected onto the chessboard, which allows correspondences between the camera, projector and the chessboard to be automatically extracted. The method is experimentally validated on the procam system, which is shown to be comparable in accuracy with existing multi-pose approaches.
  arXiv  Detail & Related papers  (2021-02-22T22:53:29Z)
• Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups [68.8204255655161]
  We present a method to calibrate the parameters of any pair of sensors involving LiDARs, monocular or stereo cameras. The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups.
  arXiv  Detail & Related papers  (2021-01-12T12:02:26Z)
• Zero-Shot Calibration of Fisheye Cameras [0.010956300138340428]
  The proposed method estimates camera parameters from the horizontal and vertical field of view information of the camera without any image acquisition. The method is particularly useful for wide-angle or fisheye cameras that have large image distortion.
  arXiv  Detail & Related papers  (2020-11-30T08:10:24Z)
• Infrastructure-based Multi-Camera Calibration using Radial Projections [117.22654577367246]
  Pattern-based calibration techniques can be used to calibrate the intrinsics of the cameras individually. Infrastucture-based calibration techniques are able to estimate the extrinsics using 3D maps pre-built via SLAM or Structure-from-Motion. We propose to fully calibrate a multi-camera system from scratch using an infrastructure-based approach.
  arXiv  Detail & Related papers  (2020-07-30T09:21:04Z)
• On-line non-overlapping camera calibration net [2.4569090161971743]
  We propose an on-line method of the inter-camera pose estimation. Experiments with simulations and the KITTI dataset show the proposed method to be effective in simulation.
  arXiv  Detail & Related papers  (2020-02-19T04:59:11Z)

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.