Deep learning for occlusion aware RGB-D image completion for structured light measurements

Florian Siegmund; Marcel Spehr; Daniel Höhne; Gunther Notni

doi:10.1117/12.2557826

1 June 2020 Deep learning for occlusion aware RGB-D image completion for structured light measurements

Florian Siegmund, Marcel Spehr, Daniel Höhne, Gunther Notni

Proceedings Volume 11396, Computational Imaging V; 113960G (2020) https://doi.org/10.1117/12.2557826
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

In our work we propose a deep learning solution to complete RGB-D images that are acquired by a NIR structured light scanner with an additional RGB camera that measures the visible spectrum. Building on works on image inpainting, we designed and trained a neural network architecture that takes the available fringe and color images as well as the reliably measured depth information and completes the depth images. We particularly focus on occlusion-caused image artifacts that naturally occur due to geometric visibility constraints. Hence, we are able to reconstruct a dense depth image from the viewpoint of the RGB camera, which can be used for further post-processing and visualization purposes.

Citation Download Citation

Florian Siegmund, Marcel Spehr, Daniel Höhne, and Gunther Notni "Deep learning for occlusion aware RGB-D image completion for structured light measurements", Proc. SPIE 11396, Computational Imaging V, 113960G (1 June 2020); https://doi.org/10.1117/12.2557826

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