Dynamic Event Camera Calibration
- URL: http://arxiv.org/abs/2107.06749v1
- Date: Wed, 14 Jul 2021 14:52:58 GMT
- Title: Dynamic Event Camera Calibration
- Authors: Kun Huang, Yifu Wang and Laurent Kneip
- Abstract summary: We present the first dynamic event camera calibration algorithm.
It calibrates directly from events captured during relative motion between camera and calibration pattern.
As demonstrated through our results, the obtained calibration method is highly convenient and reliably calibrates from data sequences spanning less than 10 seconds.
- Score: 27.852239869987947
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Camera calibration is an important prerequisite towards the solution of 3D
computer vision problems. Traditional methods rely on static images of a
calibration pattern. This raises interesting challenges towards the practical
usage of event cameras, which notably require image change to produce
sufficient measurements. The current standard for event camera calibration
therefore consists of using flashing patterns. They have the advantage of
simultaneously triggering events in all reprojected pattern feature locations,
but it is difficult to construct or use such patterns in the field. We present
the first dynamic event camera calibration algorithm. It calibrates directly
from events captured during relative motion between camera and calibration
pattern. The method is propelled by a novel feature extraction mechanism for
calibration patterns, and leverages existing calibration tools before
optimizing all parameters through a multi-segment continuous-time formulation.
As demonstrated through our results on real data, the obtained calibration
method is highly convenient and reliably calibrates from data sequences
spanning less than 10 seconds.
Related papers
- CasCalib: Cascaded Calibration for Motion Capture from Sparse Unsynchronized Cameras [18.51320244029833]
It is now possible to estimate 3D human pose from monocular images with off-the-shelf 3D pose estimators.
Many practical applications require fine-grained absolute pose information for which multi-view cues and camera calibration are necessary.
Our goal is full automation, which includes temporal synchronization, as well as intrinsic and extrinsic camera calibration.
arXiv Detail & Related papers (2024-05-10T23:02:23Z) - C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion [54.81141583427542]
In deep learning, test-time adaptation has gained attention as a method for model fine-tuning without the need for labeled data.
This paper explores calibration during test-time prompt tuning by leveraging the inherent properties of CLIP.
We present a novel method, Calibrated Test-time Prompt Tuning (C-TPT), for optimizing prompts during test-time with enhanced calibration.
arXiv Detail & Related papers (2024-03-21T04:08:29Z) - E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras [18.54225086007182]
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) - 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) - A Deep Perceptual Measure for Lens and Camera Calibration [35.03926427249506]
In place of the traditional multi-image calibration process, we propose to infer the camera calibration parameters directly from a single image.
We train this network using automatically generated samples from a large-scale panorama dataset.
We conduct a large-scale human perception study where we ask participants to judge the realism of 3D objects composited with correct and biased camera calibration parameters.
arXiv Detail & Related papers (2022-08-25T18:40:45Z) - 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) - 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) - Uncertainty Quantification and Deep Ensembles [79.4957965474334]
We show that deep-ensembles do not necessarily lead to improved calibration properties.
We show that standard ensembling methods, when used in conjunction with modern techniques such as mixup regularization, can lead to less calibrated models.
This text examines the interplay between three of the most simple and commonly used approaches to leverage deep learning when data is scarce.
arXiv Detail & Related papers (2020-07-17T07:32:24Z)
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.