UHRNet: A Deep Learning-Based Method for Accurate 3D Reconstruction from a Single Fringe-Pattern
This work addresses the need for more accurate single-shot 3D reconstruction in profilometry, though it appears incremental as it builds on existing CNN-based methods.
The paper tackles the problem of improving the accuracy of 3D reconstruction from a single fringe pattern in Fringe Projection Profilometry, achieving an average RMSE of 0.443 mm, which is 41.13% of a UNet method and 33.31% of a prior method.
The quick and accurate retrieval of an object height from a single fringe pattern in Fringe Projection Profilometry has been a topic of ongoing research. While a single shot fringe to depth CNN based method can restore height map directly from a single pattern, its accuracy is currently inferior to the traditional phase shifting technique. To improve this method's accuracy, we propose using a U shaped High resolution Network (UHRNet). The network uses UNet encoding and decoding structure as backbone, with Multi-Level convolution Block and High resolution Fusion Block applied to extract local features and global features. We also designed a compound loss function by combining Structural Similarity Index Measure Loss (SSIMLoss) function and chunked L2 loss function to improve 3D reconstruction details.We conducted several experiments to demonstrate the validity and robustness of our proposed method. A few experiments have been conducted to demonstrate the validity and robustness of the proposed method, The average RMSE of 3D reconstruction by our method is only 0.443(mm). which is 41.13% of the UNet method and 33.31% of Wang et al hNet method. Our experimental results show that our proposed method can increase the accuracy of 3D reconstruction from a single fringe pattern.