Self-Supervised Dual-Frequency Phase Decomposition for Single-Shot Composite Fringe Projection Profilometry

• URL: http://arxiv.org/abs/2606.21027v1
• Date: Fri, 19 Jun 2026 01:34:52 GMT
• Title: Self-Supervised Dual-Frequency Phase Decomposition for Single-Shot Composite Fringe Projection Profilometry
• Abstract summary: Single-shot fringe projection profilometry (FPP) has been actively studied for real-time measurement, dynamic object reconstruction, and motion-sensitive environments.<n> Composite fringe patterns are advantageous in single-shot FPP because multiple frequency components can be encoded in a single pattern, enabling phase ambiguity resolution.<n>We propose a self-supervised phase refinement framework for single-shot composite fringe patterns without requiring phase or depth labels.
• Score: 0.866627581195388
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Single-shot fringe projection profilometry (FPP) has been actively studied for real-time measurement, dynamic object reconstruction, and motion-sensitive environments. Composite fringe patterns are advantageous in single-shot FPP because multiple frequency components can be encoded in a single pattern, enabling phase ambiguity resolution. Existing approaches mainly rely on Fourier transform-based methods or supervised deep learning methods. However, Fourier transform-based methods often suffer from limited accuracy and degraded performance in complex regions, while supervised methods require dense phase or depth labels, which are costly to obtain. In this work, we propose a self-supervised phase refinement framework for single-shot composite fringe patterns without requiring phase or depth labels. The proposed method exploits the scale and direction relationships between low- and high-frequency phase gradients, improving the reliability of phase separation. We also introduce a soft edge consistency loss to preserve object boundaries and fine geometric structures. Experimental results show that the proposed method achieves MAE_z and RMSE_z of 0.367 mm and 1.804 mm, respectively, outperforming the best-performing transform-based baseline, which obtains 0.402 mm and 2.785 mm. The proposed method also improves the valid-pixel ratio from 84.75 % to 95.07 %. These results demonstrate the effectiveness of self-supervised dual-frequency phase refinement for reliable single-shot 3D reconstruction without ground-truth label supervision.

Related papers

• LCPNet: Latent Consistent Proximal Unfolding Network for Infrared Small Target Detection [50.3892176822492]
  Infrared small target detection (IRSTD) aims to identify long distance small targets from complex infrared backgrounds.<n>Deep learning methods have improved IRSTD by learning discriminative image-to-mask mappings.<n>Deep unfolding methods partially address this issue by embedding model-driven iterations into neural networks.<n>We propose Latent Consistent Proximal unfolding network (LCPNet) to address these limitations.
  arXiv  Detail & Related papers  (2026-07-06T02:15:09Z)
• Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems [48.501415601682815]
  Image restoration faces a fundamental tradeoff: methods that minimize error produce blurry reconstructions, while those that maximize perceptual quality yield sharp but less faithful images.<n>We show that flow map models implicitly define a one- parameter family of denoisers that continuously spans the distortion perception frontier.<n>Within a Plug-and-Play solver, the same mechanism extends to general inverse problems, where it controls a tradeoff between perceptual alignment and data consistency.
  arXiv  Detail & Related papers  (2026-06-18T05:15:43Z)
• Diffusion Image Generation with Explicit Modeling of Data Manifold Geometry [64.61931485161833]
  Image generative models aim to sample data points from the underlying data manifold.<n>We propose the Data Manifold-aware Image diffusioN moDel (MIND)<n>MIND integrates discrete patch tokenization into the score function of a continuous diffusion model.
  arXiv  Detail & Related papers  (2026-05-25T08:43:14Z)
• Towards Generalized Image Manipulation Localization via Score-based Model [38.03898206912958]
  We propose DiffIML, a novel framework that introduces score-based generative modeling to IML.<n>Under this formulation, diffusion models serve as an effective numerical solver for the learned score function.<n>Experiments on eight non-generative and three generative benchmarks demonstrate that DiffIML consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-05-16T08:39:54Z)
• Self-Supervised Angular Deblurring in Photoacoustic Reconstruction via Noisier2Inverse [2.812291464467386]
  Photoacoustic tomography (PAT) is an emerging imaging modality that combines optical contrast and ultrasonic resolution.<n>We propose a self-supervised reconstruction method based on Noisier2Inverse that addresses finite-size detector effects without requiring ground-truth data.<n>Our approach operates directly on noisy measurements and learns to recover high-quality PAT images in a ground-truth-free manner.
  arXiv  Detail & Related papers  (2026-04-17T04:07:11Z)
• Error-Propagation-Free Learned Video Compression With Dual-Domain Progressive Temporal Alignment [92.57576987521107]
  We propose a novel unifiedtransform framework with dual-domain progressive temporal alignment and quality-conditioned mixture-of-expert (QCMoE)<n>QCMoE allows continuous and consistent rate control with appealing R-D performance.<n> Experimental results show that the proposed method achieves competitive R-D performance compared with the state-of-the-arts.
  arXiv  Detail & Related papers  (2025-12-11T09:14:51Z)
• A Sub-Pixel Multimodal Optical Remote Sensing Images Matching Method [2.9300891362823425]
  We propose a phase consistency weighted least absolute deviation (PCWLAD) sub-pixel template matching method to improve the matching accuracy of multimodal optical images.<n>To evaluate the performance of PCWLAD, we created three types of image datasets: visible to infrared Landsat images, visible to near-infrared close-range images, and visible to infrared uncrewed aerial vehicle (UAV) images.
  arXiv  Detail & Related papers  (2025-08-14T02:49:19Z)
• Few-Step Diffusion via Score identity Distillation [67.07985339442703]
  Diffusion distillation has emerged as a promising strategy for accelerating text-to-image (T2I) diffusion models.<n>Existing methods rely on real or teacher-synthesized images to perform well when distilling high-resolution T2I diffusion models.<n>We propose two new guidance strategies: Zero-CFG, which disables CFG in the teacher and removes text conditioning in the fake score network, and Anti-CFG, which applies negative CFG in the fake score network.
  arXiv  Detail & Related papers  (2025-05-19T03:45:16Z)
• FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust Fusion [92.4205087439928]
  Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability.<n>We propose the Self-supervised Transfer (PST) and the FrequencyDe-coupled Fusion module (FreDF)<n>PST establishes cross-modal knowledge transfer through latent space alignment with image foundation models, effectively mitigating data scarcity.<n>FreDF explicitly decouples high-frequency edge features from low-frequency structural components, resolving modality-specific frequency mismatches.<n>This combined approach enables FUSE to construct a universal image-event that only requires lightweight decoder adaptation for target datasets.
  arXiv  Detail & Related papers  (2025-03-25T15:04:53Z)
• Straighter Flow Matching via a Diffusion-Based Coupling Prior [29.38872850098043]
  We propose Straighter trajectories of Flow Matching (StraightFM)<n>It straightens trajectories with the coupling strategy from the entire distribution level.<n> Experimental results on pixel space and latent space show that StraightFM yields attractive samples within 5 steps.
  arXiv  Detail & Related papers  (2023-11-28T06:19:30Z)
• Single-shot Phase Retrieval from a Fractional Fourier Transform Perspective [12.490990352972695]
  We present a novel single-shot phase retrieval paradigm from a fractional Fourier transform perspective. The intensity measurement in the FrFT domain proves highly effective in alleviating the ambiguities of phase retrieval. The proposed self-supervised reconstruction approach harnesses the fast discrete algorithm of FrFT alongside untrained neural network priors.
  arXiv  Detail & Related papers  (2023-11-18T03:11:31Z)
• Self-supervised phase unwrapping in fringe projection profilometry [0.0]
  A novel self-supervised phase unwrapping method for single-camera fringe projection profilometry is proposed. The trained network can retrieve the absolute fringe order from one phase map of 64-period and overperform DF-TPU approaches in terms of depth accuracy. Experimental results demonstrate the validation of the proposed method on real scenes of motion blur, isolated objects, low reflectivity, and phase discontinuity.
  arXiv  Detail & Related papers  (2023-02-13T14:16:34Z)

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.