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MIMONet: Structured-light 3D shape reconstruction by a multi-input multi-output network

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Abstract

Reconstructing 3D geometric representation of objects with deep learning frameworks has recently gained a great deal of interest in numerous fields. The existing deep-learning-based 3D shape reconstruction techniques generally use a single red–green–blue (RGB) image, and the depth reconstruction accuracy is often highly limited due to a variety of reasons. We present a 3D shape reconstruction technique with an accuracy enhancement strategy by integrating the structured-light scheme with deep convolutional neural networks (CNNs). The key idea is to transform multiple (typically two) grayscale images consisting of fringe and/or speckle patterns into a 3D depth map using an end-to-end artificial neural network. Distinct from the existing autoencoder-based networks, the proposed technique reconstructs the 3D shape of target using a refinement approach that fuses multiple feature maps to obtain multiple outputs with an accuracy-enhanced final output. A few experiments have been conducted to verify the robustness and capabilities of the proposed technique. The findings suggest that the proposed network approach can be a promising 3D reconstruction technique for future academic research and industrial applications.

© 2021 Optical Society of America

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Data underlying the results presented in this paper are not publicly available at this time but can be obtained from the authors upon reasonable request.

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