Feature Extraction Reimagined: Achieving Superior Accuracy in Camera Calibration
- URL: http://arxiv.org/abs/2410.13371v2
- Date: Fri, 15 Nov 2024 03:07:13 GMT
- Title: Feature Extraction Reimagined: Achieving Superior Accuracy in Camera Calibration
- Authors: Zezhun Shi,
- Abstract summary: This paper focuses on improving the accuracy of feature extraction, which is a key step in calibration.
We introduce a novel dynamic calibration target that synthesizes multiple checkerboard patterns of different angle around pattern center.
We also propose a novel cost function of feature refinement that accounts for defocus effect, offering a more physically realistic model.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Camera calibration is crucial for 3D vision applications. This paper focuses on improving the accuracy of feature extraction, which is a key step in calibration. We address the aliasing problem of star-shaped pattern by introducing a novel dynamic calibration target that synthesizes multiple checkerboard patterns of different angle around pattern center, which significantly improves feature refinement accuracy. Additionally, we propose a novel cost function of feature refinement that accounts for defocus effect, offering a more physically realistic model compared to existing symmetry based method, experiment on a large dataset demonstrate significant improvements in calibration accuracy with reduced computation time. Our code is available from https://github.com/spdfghi/Feature-Extraction-Reimagined-Achieving-Superior-Accuracy-in-Camera-Calib ration.git.
Related papers
- Camera Calibration via Circular Patterns: A Comprehensive Framework with Measurement Uncertainty and Unbiased Projection Model [19.3491941784855]
We propose an unbiased projection model of the circular pattern and demonstrate its superior accuracy compared to the checkerboard.<n>We also introduce uncertainty into circular patterns to enhance calibration robustness and completeness.<n>The core concept of this approach is to model the boundary points of a two-dimensional shape as a Markov random field, considering its connectivity.
arXiv Detail & Related papers (2025-06-20T08:46:48Z) - AlignDiff: Learning Physically-Grounded Camera Alignment via Diffusion [0.5277756703318045]
We introduce a novel framework that addresses camera intrinsic and extrinsic parameters using a generic ray camera model.<n>Unlike previous approaches, AlignDiff shifts focus from semantic to geometric features, enabling more accurate modeling of local distortions.<n>Our experiments demonstrate that the proposed method significantly reduces the angular error of estimated ray bundles by 8.2 degrees and overall calibration accuracy, outperforming existing approaches on challenging, real-world datasets.
arXiv Detail & Related papers (2025-03-27T14:59:59Z) - 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) - SHIC: Shape-Image Correspondences with no Keypoint Supervision [106.99157362200867]
Canonical surface mapping generalizes keypoint detection by assigning each pixel of an object to a corresponding point in a 3D template.
Popularised by DensePose for the analysis of humans, authors have attempted to apply the concept to more categories.
We introduce SHIC, a method to learn canonical maps without manual supervision which achieves better results than supervised methods for most categories.
arXiv Detail & Related papers (2024-07-26T17:58:59Z) - RoFIR: Robust Fisheye Image Rectification Framework Impervious to Optical Center Deviation [88.54817424560056]
We propose a distortion vector map (DVM) that measures the degree and direction of local distortion.
By learning the DVM, the model can independently identify local distortions at each pixel without relying on global distortion patterns.
In the pre-training stage, it predicts the distortion vector map and perceives the local distortion features of each pixel.
In the fine-tuning stage, it predicts a pixel-wise flow map for deviated fisheye image rectification.
arXiv Detail & Related papers (2024-06-27T06:38:56Z) - Towards Robust and Expressive Whole-body Human Pose and Shape Estimation [51.457517178632756]
Whole-body pose and shape estimation aims to jointly predict different behaviors of the entire human body from a monocular image.
Existing methods often exhibit degraded performance under the complexity of in-the-wild scenarios.
We propose a novel framework to enhance the robustness of whole-body pose and shape estimation.
arXiv Detail & Related papers (2023-12-14T08:17:42Z) - RCDN -- Robust X-Corner Detection Algorithm based on Advanced CNN Model [3.580983453285039]
We present a novel detection algorithm which can maintain high sub-pixel precision on inputs under multiple interferences.
The whole algorithm, adopting a coarse-to-fine strategy, contains a X-corner detection network and three post-processing techniques.
Evaluations on real and synthetic images indicate that the presented algorithm has the higher detection rate, sub-pixel accuracy and robustness than other commonly used methods.
arXiv Detail & Related papers (2023-07-07T10:40:41Z) - 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) - Neural Lens Modeling [50.57409162437732]
NeuroLens is a neural lens model for distortion and vignetting that can be used for point projection and ray casting.
It can be used to perform pre-capture calibration using classical calibration targets, and can later be used to perform calibration or refinement during 3D reconstruction.
The model generalizes across many lens types and is trivial to integrate into existing 3D reconstruction and rendering systems.
arXiv Detail & Related papers (2023-04-10T20:09:17Z) - Parallax-Tolerant Unsupervised Deep Image Stitching [57.76737888499145]
We propose UDIS++, a parallax-tolerant unsupervised deep image stitching technique.
First, we propose a robust and flexible warp to model the image registration from global homography to local thin-plate spline motion.
To further eliminate the parallax artifacts, we propose to composite the stitched image seamlessly by unsupervised learning for seam-driven composition masks.
arXiv Detail & Related papers (2023-02-16T10:40:55Z) - CCDN: Checkerboard Corner Detection Network for Robust Camera
Calibration [10.614480156920935]
checkerboard corner detection network and some post-processing techniques.
Network model is a fully convolutional network with improvements of loss function and learning rate.
In order to remove the false positives, we employ three post-processing techniques including threshold related to maximum response, non-maximum suppression, and clustering.
arXiv Detail & Related papers (2023-02-10T07:47:44Z) - TartanCalib: Iterative Wide-Angle Lens Calibration using Adaptive
SubPixel Refinement of AprilTags [23.568127229446965]
Calibrating wide-angle lenses with current state-of-the-art techniques yields poor results due to extreme distortion at the edge.
We present our methodology for accurate wide-angle calibration.
arXiv Detail & Related papers (2022-10-05T18:57:07Z) - Pixel2Mesh++: 3D Mesh Generation and Refinement from Multi-View Images [82.32776379815712]
We study the problem of shape generation in 3D mesh representation from a small number of color images with or without camera poses.
We adopt to further improve the shape quality by leveraging cross-view information with a graph convolution network.
Our model is robust to the quality of the initial mesh and the error of camera pose, and can be combined with a differentiable function for test-time optimization.
arXiv Detail & Related papers (2022-04-21T03:42:31Z) - A Model for Multi-View Residual Covariances based on Perspective
Deformation [88.21738020902411]
We derive a model for the covariance of the visual residuals in multi-view SfM, odometry and SLAM setups.
We validate our model with synthetic and real data and integrate it into photometric and feature-based Bundle Adjustment.
arXiv Detail & Related papers (2022-02-01T21:21:56Z) - Pixel-Perfect Structure-from-Motion with Featuremetric Refinement [96.73365545609191]
We refine two key steps of structure-from-motion by a direct alignment of low-level image information from multiple views.
This significantly improves the accuracy of camera poses and scene geometry for a wide range of keypoint detectors.
Our system easily scales to large image collections, enabling pixel-perfect crowd-sourced localization at scale.
arXiv Detail & Related papers (2021-08-18T17:58:55Z) - Dynamic Event Camera Calibration [27.852239869987947]
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.
arXiv Detail & Related papers (2021-07-14T14:52:58Z) - 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) - ACSC: Automatic Calibration for Non-repetitive Scanning Solid-State
LiDAR and Camera Systems [11.787271829250805]
Solid-State LiDAR (SSL) enables low-cost and efficient obtainment of 3D point clouds from the environment.
We propose a fully automatic calibration method for the non-repetitive scanning SSL and camera systems.
We evaluate the proposed method on different types of LiDAR and camera sensor combinations in real conditions.
arXiv Detail & Related papers (2020-11-17T09:11:28Z) - Superaccurate Camera Calibration via Inverse Rendering [0.19336815376402716]
We propose a new method for camera calibration using the principle of inverse rendering.
Instead of relying solely on detected feature points, we use an estimate of the internal parameters and the pose of the calibration object to implicitly render a non-photorealistic equivalent of the optical features.
arXiv Detail & Related papers (2020-03-20T10:26:16Z) - Calibrating Deep Neural Networks using Focal Loss [77.92765139898906]
Miscalibration is a mismatch between a model's confidence and its correctness.
We show that focal loss allows us to learn models that are already very well calibrated.
We show that our approach achieves state-of-the-art calibration without compromising on accuracy in almost all cases.
arXiv Detail & Related papers (2020-02-21T17:35:50Z)
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.