Hybrid calibration procedure for fringe projection profilometry based on
stereo-vision and polynomial fitting
- URL: http://arxiv.org/abs/2003.04168v1
- Date: Mon, 9 Mar 2020 14:25:03 GMT
- Title: Hybrid calibration procedure for fringe projection profilometry based on
stereo-vision and polynomial fitting
- Authors: Raul Vargas, Andres G. Marrugo, Song Zhang, Lenny A. Romero
- Abstract summary: The key to accurate 3D shape measurement in Fringe Projection Profilometry (FPP) is the proper calibration of the measurement system.
Current calibration techniques rely on phase-coordinate mapping (PCM) or back-projection stereo-vision (SV) methods.
We propose a hybrid calibration method that leverages the SV calibration approach using a PCM method to achieve higher accuracy.
- Score: 4.291718205405102
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The key to accurate 3D shape measurement in Fringe Projection Profilometry
(FPP) is the proper calibration of the measurement system. Current calibration
techniques rely on phase-coordinate mapping (PCM) or back-projection
stereo-vision (SV) methods. PCM methods are cumbersome to implement as they
require precise positioning of the calibration target relative to the FPP
system but produce highly accurate measurements within the calibration volume.
SV methods generally do not achieve the same accuracy level. However, the
calibration is more flexible in that the calibration target can be arbitrarily
positioned. In this work, we propose a hybrid calibration method that leverages
the SV calibration approach using a PCM method to achieve higher accuracy. The
method has the flexibility of SV methods, is robust to lens distortions, and
has a simple relation between the recovered phase and the metric coordinates.
Experimental results show that the proposed Hybrid method outperforms the SV
method in terms of accuracy and reconstruction time due to its low
computational complexity.
Related papers
- Optimal Pose Guidance for Stereo Calibration in 3D Deformation Measurement [33.47288558214902]
The aim of this study is to develop an interactive calibration framework that automatically generates the next optimal pose.<n> integrated with this method is a user-friendly graphical interface, which guides even non-expert users to capture qualified calibration images.
arXiv Detail & Related papers (2025-11-23T07:10:07Z) - Generic Calibration: Pose Ambiguity/Linear Solution and Parametric-hybrid Pipeline [18.23955853642985]
This paper reveals a pose ambiguity in the pose solutions of generic calibration methods.<n>A global optimization hybrid calibration method is introduced to integrate generic and parametric models together.<n> Simulation and real-world experimental results demonstrate that the generic-parametric hybrid calibration method consistently excels across various lens types and noise contamination.
arXiv Detail & Related papers (2025-08-10T07:36:48Z) - Adaptive Set-Mass Calibration with Conformal Prediction [60.47079469141295]
We develop a new calibration procedure that starts with conformal prediction to obtain a set of labels that gives the desired coverage.<n>We then instantiate two simple post-hoc calibrators: a mass normalization and a temperature scaling-based rule, tuned to the conformal constraint.
arXiv Detail & Related papers (2025-05-21T12:18:15Z) - Marker-Based Extrinsic Calibration Method for Accurate Multi-Camera 3D Reconstruction [0.23749905164931198]
In this paper, we introduce an iterative extrinsic calibration method that leverages the geometric constraints provided by a three-dimensional marker.<n>We validate our method comprehensively in both controlled environments and practical real-world settings within the Tech4Diet project.<n> Experimental results demonstrate substantial reductions in alignment errors, facilitating accurate and reliable 3D reconstructions.
arXiv Detail & Related papers (2025-05-05T10:21:41Z) - High-precision visual navigation device calibration method based on collimator [7.067969652798468]
This study presents a collimator-based calibration method and system.
Based on the optical characteristics of the collimator, a single-image camera calibration algorithm is introduced.
Experimental results demonstrate that the proposed method achieves accuracy and stability comparable to traditional multi-image calibration techniques.
arXiv Detail & Related papers (2025-02-25T09:18:45Z) - Combining Priors with Experience: Confidence Calibration Based on Binomial Process Modeling [3.4580564656984736]
Existing confidence calibration methods mostly use statistical techniques to estimate the calibration curve from data.
A new calibration metric ($TCE_bpm$), which leverages the estimated calibration curve to estimate the true calibration error (TCE), is designed.
The effectiveness of our calibration method and metric are verified in real-world and simulated data.
arXiv Detail & Related papers (2024-12-14T03:04:05Z) - Calibration by Distribution Matching: Trainable Kernel Calibration
Metrics [56.629245030893685]
We introduce kernel-based calibration metrics that unify and generalize popular forms of calibration for both classification and regression.
These metrics admit differentiable sample estimates, making it easy to incorporate a calibration objective into empirical risk minimization.
We provide intuitive mechanisms to tailor calibration metrics to a decision task, and enforce accurate loss estimation and no regret decisions.
arXiv Detail & Related papers (2023-10-31T06:19:40Z) - Adaptive Calibrator Ensemble for Model Calibration under Distribution
Shift [23.794897699193875]
adaptive calibrator ensemble (ACE) calibrates OOD datasets whose difficulty is usually higher than the calibration set.
ACE generally improves the performance of a few state-of-the-art calibration schemes on a series of OOD benchmarks.
arXiv Detail & Related papers (2023-03-09T15:22:02Z) - Bag of Tricks for In-Distribution Calibration of Pretrained Transformers [8.876196316390493]
We present an empirical study on confidence calibration for pre-trained language models (PLMs)
We find that the ensemble model overfitted to the training set shows sub-par calibration performance.
We propose the Calibrated PLM (CALL), a combination of calibration techniques.
arXiv Detail & Related papers (2023-02-13T21:11:52Z) - A Unifying Theory of Distance from Calibration [9.959025631339982]
There is no consensus on how to quantify the distance from perfect calibration.
We propose a ground-truth notion of distance from calibration, inspired by the literature on property testing.
Applying our framework, we identify three calibration measures that are consistent and can be estimated efficiently.
arXiv Detail & Related papers (2022-11-30T10:38:24Z) - Modular Conformal Calibration [80.33410096908872]
We introduce a versatile class of algorithms for recalibration in regression.
This framework allows one to transform any regression model into a calibrated probabilistic model.
We conduct an empirical study of MCC on 17 regression datasets.
arXiv Detail & Related papers (2022-06-23T03:25:23Z) - Hidden Heterogeneity: When to Choose Similarity-Based Calibration [12.788224825185633]
Black-box calibration methods are unable to detect subpopulations where calibration could improve prediction accuracy.
The paper proposes a quantitative measure for hidden heterogeneity (HH)
Experiments show that the improvements in calibration achieved by similarity-based calibration methods correlate with the amount of HH present and, given sufficient calibration data, generally exceed calibration achieved by global methods.
arXiv Detail & Related papers (2022-02-03T20:43:25Z) - Parameterized Temperature Scaling for Boosting the Expressive Power in
Post-Hoc Uncertainty Calibration [57.568461777747515]
We introduce a novel calibration method, Parametrized Temperature Scaling (PTS)
We demonstrate that the performance of accuracy-preserving state-of-the-art post-hoc calibrators is limited by their intrinsic expressive power.
We show with extensive experiments that our novel accuracy-preserving approach consistently outperforms existing algorithms across a large number of model architectures, datasets and metrics.
arXiv Detail & Related papers (2021-02-24T10:18:30Z) - Localized Calibration: Metrics and Recalibration [133.07044916594361]
We propose a fine-grained calibration metric that spans the gap between fully global and fully individualized calibration.
We then introduce a localized recalibration method, LoRe, that improves the LCE better than existing recalibration methods.
arXiv Detail & Related papers (2021-02-22T07:22:12Z) - 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) - Unsupervised Calibration under Covariate Shift [92.02278658443166]
We introduce the problem of calibration under domain shift and propose an importance sampling based approach to address it.
We evaluate and discuss the efficacy of our method on both real-world datasets and synthetic datasets.
arXiv Detail & Related papers (2020-06-29T21:50:07Z)
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.