Version 1
: Received: 23 September 2024 / Approved: 24 September 2024 / Online: 25 September 2024 (11:35:54 CEST)
How to cite:
Sepanj, M.; Moradi, S.; Nazemi, A.; Preston, C.; Lee, A. M. D.; Fieguth, P. From Single Shot to Structure: End-to-End Network based Deflectometry for Specular Free-Form Surface Reconstruction. Preprints2024, 2024091851. https://doi.org/10.20944/preprints202409.1851.v1
Sepanj, M.; Moradi, S.; Nazemi, A.; Preston, C.; Lee, A. M. D.; Fieguth, P. From Single Shot to Structure: End-to-End Network based Deflectometry for Specular Free-Form Surface Reconstruction. Preprints 2024, 2024091851. https://doi.org/10.20944/preprints202409.1851.v1
Sepanj, M.; Moradi, S.; Nazemi, A.; Preston, C.; Lee, A. M. D.; Fieguth, P. From Single Shot to Structure: End-to-End Network based Deflectometry for Specular Free-Form Surface Reconstruction. Preprints2024, 2024091851. https://doi.org/10.20944/preprints202409.1851.v1
APA Style
Sepanj, M., Moradi, S., Nazemi, A., Preston, C., Lee, A. M. D., & Fieguth, P. (2024). From Single Shot to Structure: End-to-End Network based Deflectometry for Specular Free-Form Surface Reconstruction. Preprints. https://doi.org/10.20944/preprints202409.1851.v1
Chicago/Turabian Style
Sepanj, M., Anthony M. D. Lee and Paul Fieguth. 2024 "From Single Shot to Structure: End-to-End Network based Deflectometry for Specular Free-Form Surface Reconstruction" Preprints. https://doi.org/10.20944/preprints202409.1851.v1
Abstract
Deflectometry is a key component in the precise measurement of specular (mirrored) surfaces, however traditional methods often lack an end-to-end approach that performs 3D reconstruction in a single shot with high accuracy and generalizes across different free-form surfaces. This paper introduces a novel deep neural network (DNN)-based approach for end-to-end 3D reconstruction of free-form specular surfaces using single-shot deflectometry. Our proposed network, VUDNet, innovatively combines discriminative and generative components to accurately interpret orthogonal fringe patterns and generate high-fidelity 3D surface reconstructions. By leveraging a hybrid architecture integrating a Variational Autoencoder (VAE) and a modified U-Net, VUDNet excels in both depth estimation and detail refinement, achieving superior performance in challenging environments. Extensive data simulation using Blender, leading to a dataset which we are making available, ensures robust training and enables the network to generalize across diverse scenarios. Experimental results demonstrate the strong performance of VUDNet, setting a new standard for 3D surface reconstruction. The implementation of VUDNet is available on GitHub at https://github.com/hadious/VUDNet.
Keywords
Deflectometry, Specular Surface Reconstruction, Deep Neural Networks, Single-Shot Measurement, Data Simulation, Variational Autoencoder (VAE)
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
