# ADVSS Block

Reconstructing a 3D surface from a single high-frequency fringe image is hard because long-range spatial consistency and fine geometric detail must both survive. Convolutional networks model long-range context weakly, and window-based Transformers build global interactions only gradually with depth. A hybrid Mamba-Transformer network adds visual state-space blocks at the intermediate-resolution encoder stage of a Swin-style U-shaped architecture, with an axial-dilated block that strengthens directional fringe features before selective scanning. On a public fringe-projection dataset, using only one deformed fringe image, it reaches 0.548 mm RMSE and 0.9985 SSIM, cutting RMSE by 65.2% and 59.6% relative to UHRNet and HiDNet.

Reconstructing a 3D surface from a single high-frequency fringe image is hard because long-range spatial consistency and fine geometric detail must both survive. Convolutional networks model long-range context weakly, and window-based Transformers build global interactions only gradually with depth. A hybrid Mamba-Transformer network adds visual state-space blocks at the intermediate-resolution encoder stage of a Swin-style U-shaped architecture, with an axial-dilated block that strengthens directional fringe features before selective scanning. On a public fringe-projection dataset, using only one deformed fringe image, it reaches 0.548 mm RMSE and 0.9985 SSIM, cutting RMSE by 65.2% and 59.6% relative to UHRNet and HiDNet.