Fringe Projection Profilometry

Latest papers 12

1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

RACE-FPP: A Robust AI-assisted Characterisation Enhancement for Fringe Projection Profilometry
Oct 6, 2026Osman Ali, Xiangjun Kong, Tibebe Yalew +2Fringe Projection Profilometry Camera Calibration
Manufacturing Metrology Team, University of Nottingham, Nottingham, United Kingdom · Taraz Metrology Ltd., Nottingham, United Kingdom
Sensor-Informed Per-Point Covariance for Structured-Light 3D Imaging
Aug 11, 2026Sehoon Tak, Jae-Sang HyunFringe Projection Profilometry Uncertainty Quantification
Department of Mechanical Engineering, Yonsei University, Seoul 03722, South Korea
Ray-based phase error correction for miniaturized DOE projector-based FPP under single-directional hyperbolic projection
Jul 16, 2026Seung-Jae Son, Yatong An, Jae-Sang HyunFringe Projection Profilometry 3D Reconstruction
School of Mechanical Engineering Yonsei University, 03722, Seoul, South Korea · Meta Reality Labs, Redmond, 98052, Washington, USA
Repairing Shape-Prior Shortcuts in Long-Range Single-Shot Fringe Projection Profilometry
Jul 10, 2026Adam Haroon, Cody Fleming, Beiwen LiFringe Projection Profilometry Metric Depth Estimation
Department of Mechanical Engineering, Iowa State University, 2529 Union Drive, Ames, 50011, Iowa, USA · College of Engineering, University of Georgia, 597 D. W. Brooks Drive, Athens, 30602, Georgia, USA
Self-Supervised Dual-Frequency Phase Decomposition for Single-Shot Composite Fringe Projection Profilometry
Jun 19, 2026Jin-Hyuk Seok, Yatong An, Jae-Sang HyunFringe Projection Profilometry Self-Supervised Learning
Contact-Based Fringe Projection Profilometry for High-Resolution 3-D Surface Measurement of Reflective and Transparent Objects
Jun 16, 2026Ingu Yeo, Hyung-Gun Chi, Jae-Sang HyunFringe Projection Profilometry Tactile Sensing
Department of Mechanical Engineering and the Yonsei Institute for Embodied Intelligence, Yonsei University, Seoul 03722, South Korea
Diagnosing Shape-Prior Shortcuts in Long-Range Single-Shot Fringe Projection Profilometry
Jun 13, 2026Adam Haroon, Anush Lakshman, Cody Fleming +1Fringe Projection Profilometry 3D Reconstruction
Department of Mechanical Engineering, Iowa State University, 2529 Union Drive, Ames, 50011, Iowa, USA · College of Engineering, University of Georgia, 597 D. W. Brooks Drive, Athens, 30602, Georgia, USA
Spatially Coupled Phase-to-Depth Calibration for Fringe Projection Profilometry
Jun 10, 2026Sehoon Tak, Jae-Sang HyunFringe Projection Profilometry Metric Depth Estimation
Department of Mechanical Engineering, Yonsei University, Seoul 03722, South Korea.
Fringe Projection Based Vision Pipeline for Autonomous Hard Drive Disassembly
Apr 19, 2026Badrinath Balasubramaniam, Vignesh Suresh, Benjamin Metcalf +1Fringe Projection Profilometry 3D Reconstruction
School of Electrical and Computer Engineering, University of Georgia, Athens, 30605, GA, USA · Alcon Research Laboratories, Fort Worth, 76134, Texas, USA · University of Georgia, Athens, 30605, GA, USA +1
High-Precision 6DOF Pose Estimation via Global Phase Retrieval in Fringe Projection Profilometry for 3D Mapping
Mar 12, 2026Sehoon Tak, Keunhee Cho, Sangpil Kim +1Object Pose Estimation Camera Pose Estimation
Department of Mechanical Engineering, Yonsei University, Seoul 03722, South Korea. · Department of Artificial Intelligence, Korea University, Seoul
Cylindrical Mechanical Projector for Omnidirectional Fringe Projection Profilometry
Mar 11, 2026Mincheol Choi, Gaeun Kim, Jae-Sang Hyun3D Reconstruction Fringe Projection Profilometry
Department of Mechanical Engineering, Yonsei University, Seoul 03722, South Korea.
LCAMV: High-Accuracy 3D Reconstruction of Color-Varying Objects Using LCA Correction and Minimum-Variance Fusion in Structured Light
Mar 11, 2026Wonbeen Oh, Jae-Sang Hyun3D Reconstruction Fringe Projection Profilometry
Department of Mechanical Engineering, Yonsei University, Seoul 03722, South Korea.

Oct 6, 2026J/K move · Enter open · S save

RACE-FPP: A Robust AI-assisted Characterisation Enhancement for Fringe Projection Profilometry

Osman Ali, Xiangjun Kong, Tibebe Yalew, Waiel Elmadih+1

Manufacturing Metrology Team, University of Nottingham, Nottingham, United Kingdom · Taraz Metrology Ltd., Nottingham, United Kingdom

Fringe Projection Profilometry Camera Calibration

Open paper AskPDF arXiv

Fringe Projection Profilometry (FPP) requires precise system characterisation to achieve reliable three-dimensional (3D) reconstructions; however, characterisation accuracy strongly depends on robust checkerboard feature localisation, which can deteriorate under challenging imaging conditions such as lens blur and characterisation target orientations. Existing deep learning-based corner detectors are typically assessed using detection metrics and camera reprojection error alone, without considering their wider impact on projector characterisation, camera-projector stereo characterisation consistency, or overall measurement accuracy. In this work, we introduce a complete FPP characterisation pipeline that incorporates deep learning-based corner detection into the standard camera characterisation workflow. We also characterise the projector by sampling phase values at the centres of the white squares in the characterisation target. Rather than treating corner detection as an isolated task, the proposed framework explicitly analyses how localisation errors propagate throughout the entire FPP characterisation chain. Performance is evaluated using detection metrics (e.g., precision and recall), camera and projector reprojection errors, and the camera and projector stereo characterisation. Across a mixed dataset of clean and degraded images, the camera reprojection error is reduced from 1.237 pixels to 0.259 pixels, while the projector reprojection error is reduced by roughly 50%. Dimensional evaluation of reconstructed artefacts shows improved geometric accuracy compared with those resulting from the conventional pipeline. Overall, the findings indicate increased robustness of system-level characterisation under challenging imaging conditions, thereby enabling more reliable industrial FPP measurements.