Digital fringe projection (DFP) enables micrometer-level 3D reconstruction, yet extending it to large-scale mapping remains challenging because six-degree-of-freedom pose estimation often cannot match the reconstruction’s precision. Conventional iterative closest point (ICP) registration becomes inefficient on multi-million-point clouds and typically relies on downsampling or feature-based selection, which can reduce local detail and degrade pose precision. Drift-correction methods improve long-term consistency but do not resolve sampling sensitivity in dense DFP point clouds. We propose a high-precision pose estimation method that augments a moving DFP system with a fixed, intrinsically calibrated global projector. Using the global projector’s phase-derived pixel constraints and a PnP-style reprojection objective, the method estimates the DFP system pose in a fixed reference frame without relying on deterministic feature extraction, and we experimentally demonstrate sampling invariance under coordinate-preserving subsampling. Experiments demonstrate sub-millimeter pose accuracy against a reference with quantified uncertainty bounds, high repeatability under aggressive subsampling, robust operation on homogeneous surfaces and low-overlap views, and reduced error accumulation when used to correct ICP-based trajectories. The method extends DFP toward accurate 3D mapping in quasi-static scenarios such as inspection and metrology, with the trade-off of time-multiplexed acquisition for the additional projector measurements.

We propose a novel omnidirectional structured light system comprising a fisheye camera and a catadioptric projector, enabling dense 3D reconstruction. In addition, we present a streamlined calibration procedure that calibrates the entire procedure using only a planar checkerboard and demonstrates robust convergence and a high success rate even under unfavorable parameter initialization. Experimental results validate the proposed method, achieving reprojection errors of 0.43 pixels for the camera and 0.63 pixels for the projector. Furthermore, the practical utility of the system is confirmed through successful dense 3D reconstructions. The source code is available at: https://github.com/GotoIlsan/Omnidirectional-Structured-Light-System.

In phase-shifting profilometry (PSP), errors can be introduced by any motion during the acquisition of fringe patterns, as it assumes both the object and the measurement system are stationary. To address this issue, we propose a pixel-wise motion-induced error reduction method when the measurement system is in motion due to a motorized system. Our proposed method introduces a novel motion-attentive phase-shifting algorithm and leverages the motor’s encoder and the pinhole model of the camera and projector. It enables accurate 3-D shape measurement with only three fringe patterns, leveraging the geometric constraints of the digital fringe projection system. We address the mismatch problem due to motion-induced camera pixel disparities and reduce phase shift errors. These processes are easy to implement and require low computational cost. Experimental results demonstrate that the presented method effectively reduces errors even in nonuniform 3-D motion.

Digital fringe projection (DFP) enables micrometer-level 3D reconstruction, yet extending it to large-scale mapping remains challenging because six-degree-of-freedom pose estimation often cannot match the reconstruction’s precision. Conventional iterative closest point (ICP) registration becomes inefficient on multi-million-point clouds and typically relies on downsampling or feature-based selection, which can reduce local detail and degrade pose precision. Drift-correction methods improve long-term consistency but do not resolve sampling sensitivity in dense DFP point clouds. We propose a high-precision pose estimation method that augments a moving DFP system with a fixed, intrinsically calibrated global projector. Using the global projector’s phase-derived pixel constraints and a PnP-style reprojection objective, the method estimates the DFP system pose in a fixed reference frame without relying on deterministic feature extraction, and we experimentally demonstrate sampling invariance under coordinate-preserving subsampling. Experiments demonstrate sub-millimeter pose accuracy against a reference with quantified uncertainty bounds, high repeatability under aggressive subsampling, robust operation on homogeneous surfaces and low-overlap views, and reduced error accumulation when used to correct ICP-based trajectories. The method extends DFP toward accurate 3D mapping in quasi-static scenarios such as inspection and metrology, with the trade-off of time-multiplexed acquisition for the additional projector measurements.

We propose a novel omnidirectional structured light system comprising a fisheye camera and a catadioptric projector, enabling dense 3D reconstruction. In addition, we present a streamlined calibration procedure that calibrates the entire procedure using only a planar checkerboard and demonstrates robust convergence and a high success rate even under unfavorable parameter initialization. Experimental results validate the proposed method, achieving reprojection errors of 0.43 pixels for the camera and 0.63 pixels for the projector. Furthermore, the practical utility of the system is confirmed through successful dense 3D reconstructions. The source code is available at: https://github.com/GotoIlsan/Omnidirectional-Structured-Light-System.

In phase-shifting profilometry (PSP), errors can be introduced by any motion during the acquisition of fringe patterns, as it assumes both the object and the measurement system are stationary. To address this issue, we propose a pixel-wise motion-induced error reduction method when the measurement system is in motion due to a motorized system. Our proposed method introduces a novel motion-attentive phase-shifting algorithm and leverages the motor’s encoder and the pinhole model of the camera and projector. It enables accurate 3-D shape measurement with only three fringe patterns, leveraging the geometric constraints of the digital fringe projection system. We address the mismatch problem due to motion-induced camera pixel disparities and reduce phase shift errors. These processes are easy to implement and require low computational cost. Experimental results demonstrate that the presented method effectively reduces errors even in nonuniform 3-D motion.

Accurate 3D reconstruction of colored objects with structured light (SL) is hindered by lateral chromatic aberration (LCA) in optical components and uneven noise characteristics across RGB channels. This paper introduces lateral chromatic aberration correction and minimum-variance fusion (LCAMV), a robust 3D reconstruction method that operates with a single projector-camera pair without additional hardware or acquisition constraints. LCAMV analytically models and pixel-wise compensates LCA in both the projector and camera, then adaptively fuses multi-channel phase data using a Poisson-Gaussian noise model and minimum-variance estimation. Unlike existing methods that require extra hardware or multiple exposures, LCAMV enables fast acquisition. Experiments on planar and non-planar colored surfaces show that LCAMV outperforms grayscale conversion and conventional channel-weighting, reducing depth error by up to 43.6\%. These results establish LCAMV as an effective solution for high-precision 3D reconstruction of nonuniformly colored objects.

Photometric stereo is a technique for estimating surface normals using images captured under varying illumination. However, conventional frame-based photometric stereo methods are limited in real-world applications due to their reliance on controlled lighting, and susceptibility to ambient illumination. To address these limitations, we propose an event-based photometric stereo system that leverages an event camera, which is effective in scenarios with continuously varying scene radiance and high dynamic range conditions. Our setup employs a single light source moving along a predefined circular trajectory, eliminating the need for multiple synchronized light sources and enabling a more compact and scalable design. We further introduce a lightweight per-pixel multi-layer neural network that directly predicts surface normals from event signals generated by intensity changes as the light source rotates, without system calibration. Experimental results on benchmark datasets and real-world data collected with our data acquisition system demonstrate the effectiveness of our method, achieving a 7.12\% reduction in mean angular error compared to existing event-based photometric stereo methods. In addition, our method demonstrates robustness in regions with sparse event activity, strong ambient illumination, and scenes affected by specularities.

Accurate 3D reconstruction of colored objects with structured light (SL) is hindered by lateral chromatic aberration (LCA) in optical components and uneven noise characteristics across RGB channels. This paper introduces lateral chromatic aberration correction and minimum-variance fusion (LCAMV), a robust 3D reconstruction method that operates with a single projector-camera pair without additional hardware or acquisition constraints. LCAMV analytically models and pixel-wise compensates LCA in both the projector and camera, then adaptively fuses multi-channel phase data using a Poisson-Gaussian noise model and minimum-variance estimation. Unlike existing methods that require extra hardware or multiple exposures, LCAMV enables fast acquisition. Experiments on planar and non-planar colored surfaces show that LCAMV outperforms grayscale conversion and conventional channel-weighting, reducing depth error by up to 43.6\%. These results establish LCAMV as an effective solution for high-precision 3D reconstruction of nonuniformly colored objects.

Fringe projection profilometry-based 3-D reconstruction of objects with high reflectivity and low surface roughness remains a significant challenge. When measuring such glossy surfaces, specular reflection and indirect illumination often lead to severe distortion or loss of the projected fringe patterns. To address these issues, we propose a latent diffusion-based structured light for reflective objects (LD-SLRO). Phase-shifted fringe images captured from highly reflective surfaces are first encoded to extract latent representations that capture surface reflectance characteristics. These latent features are then used as conditional inputs to a latent diffusion model, which probabilistically suppresses reflection-induced artifacts and recover lost fringe information, yielding high-quality fringe images. The proposed components, including the specular reflection encoder, time-variant channel affine layer, and attention modules, further improve fringe restoration quality. In addition, LD-SLRO provides high flexibility in configuring the input and output fringe sets. Experimental results demonstrate that the proposed method improves both fringe quality and 3-D reconstruction accuracy over state-of-the-art methods, reducing the average root-mean-squared error from 1.8176 mm to 0.9619 mm.