cs.CVOct 25, 2026

Hybrid Mamba-Transformer Network for Single-Shot 3D Reconstruction from Fringe Patterns

Authors: Xinjie Zhou, XiaoLei Li

Organizations: Aulin College, Northeast Forestry University, Harbin,150040, China · School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China

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Abstract

Deep learning facilitates rapid single shot three dimensional (3D) reconstruction using fringe projection profilometry (FPP), but recovering accurate surface height from a single high frequency fringe image remains challenging because both long-range spatial consistency and fine geometric details must be preserved. Convolutional networks are limited in modeling long range dependencies, whereas window-based Transformers establish global interactions only progressively with increasing depth. To address these limitations, a hybrid Mamba Transformer network is proposed for fringe to height regression. The network adopts a Swin based U shaped architecture and introduces visual state space modeling at the intermediate resolution encoder stage. An axial and dilated vision state space (ADVSS) block is further developed to enhance prescan feature encoding through local, dilated, and axial depthwise convolutions while retaining four directional selective scanning. This design combines linear complexity long range modeling with direction aware preservation of local fringe structures. Experiments on a publicly available FPP dataset show that the proposed method outperforms hNet, SwinConvUNet, UHRNet, and HiDNet in mean absolute error(MAE), root mean square error (RMSE), absolute relative error, and structural similarity index measure (SSIM). Using only one deformed fringe image, the proposed method achieves an RMSE of 0.548 mm and an SSIM of 0.9985, reducing RMSE by 65.2% and 59.6% relative to UHRNet and HiDNet, respectively. These results demonstrate the effectiveness of combining selective state space modeling with hierarchical window based attention for accurate single-shot FPP reconstruction.

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3D Reconstruction

3D reconstruction recovers a scene’s shape, appearance, and camera motion from images, video, depth, or other sensor measurements.

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Jin-Hyuk Seok, Yatong An, Jae-Sang Hyun

Fringe Projection ProfilometrySelf-Supervised Learning3D ReconstructionFrequency-Domain Image Processing
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Jul 10, 2026

Repairing Shape-Prior Shortcuts in Long-Range Single-Shot Fringe Projection Profilometry

Single-shot fringe projection profilometry (FPP) networks that regress depth directly can exploit a shape-prior shortcut, recovering depth from object boundaries rather than from fringe phase. On a photorealistic synthetic benchmark (15,600 fringe images, 50 objects at 1.5-2.1 m standoff), the best such UNet baseline plateaus at 14.54 mm object mean absolute error (MAE), and neither more data nor more capacity removes the shortcut, because neither changes the hypothesis space the optimizer searches. We introduce PhiCalNet, which outputs a wrapped-phase representation (sin⁡φ,cos⁡φ)(\sinφ, \cosφ) and maps it to depth through a fixed differentiable calibration layer, removing the shape-prior solution architecturally rather than by a loss penalty. Because the single-shot mapping is non-injective without fringe order, PhiCalNet takes the fringe order as auxiliary input, an assumption a sensitivity analysis shows tolerates realistic decoding error; a physics-informed (PINN) baseline with the same physics as a soft penalty yields no gain, isolating the architectural choice as the operative factor. PhiCalNet reduces object MAE 3.3x to 4.46 mm, its residual confined to 0.103% of pixels at the ±π\pmπ wrap discontinuity, and a three-frame extension reaches 1.16 mm. Two checks agree: interpretability makes phase the most decodable internal feature, and pixel-wise conformal uncertainty quantification, to our knowledge the first for FPP, localizes error at the same discontinuity, where rejecting the top 5% of pixels by snapshot disagreement cuts root-mean-square error by 64% versus 3.5% for the baseline.

Adam Haroon, Cody Fleming, Beiwen Li

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

Fringe Projection ProfilometryMetric Depth Estimation3D ReconstructionShortcut Learning
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Jun 13, 2026

Diagnosing Shape-Prior Shortcuts in Long-Range Single-Shot Fringe Projection Profilometry

Learning-based single-shot fringe projection profilometry (FPP) has been studied almost entirely at close range, and the networks used are evaluated only on aggregate error, leaving open whether they recover depth from fringe phase or from object-level shape cues that correlate with depth. This paper diagnoses that question mechanistically in the long-range regime (standoff beyond 1 m). Using FPP-ML-Bench, an open photorealistic synthetic benchmark (15,600 fringe images, 50 objects at 1.5--2.1 m), we first formalize why the single-shot fringe-to-depth mapping is more severely ill-posed at long range: it is non-injective without fringe-order information, and the depth error from an incorrect fringe order grows as Z2Z^2 in the working distance. Systematic ablations, extended with a multi-frame study, establish a best UNet baseline at 14.54 mm object mean absolute error (MAE), 18% of the 80 mm object depth range, with only a 1.9×\times spread across four architectures, indicating a representational rather than a capacity-bound limit. A mechanistic interpretability study, the first applied to an FPP network, localizes the cause: linear probing shows edges are 2.82×\times more decodable than depth, Grad-CAM shows attention favoring boundaries over fringes by 1.28×\times, and an in-range flat-plane test collapses a featureless plane to background depth despite valid fringes. The baseline solves the task via object-boundary shape priors rather than fringe-phase decoding. Because the shortcut is a hypothesis-space property, additional data or larger models will not remove it, motivating an architectural repair that removes the shape-prior solution by construction.

Adam Haroon, Anush Lakshman, Cody Fleming +1

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

Fringe Projection Profilometry3D ReconstructionDepth EstimationShortcut Learning
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