A simple refinement for depth information predicted with DNN

Ren Sato; Akio Kimura

doi:10.1117/12.2566610

1 June 2020 A simple refinement for depth information predicted with DNN

Ren Sato, Akio Kimura

Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 115152V (2020) https://doi.org/10.1117/12.2566610
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia

Abstract

This paper proposes a simple refinement method for improving estimated depth information with deep neural network (DNN) from a single-view RGB image. There have been a lot of effective depth prediction methods using DNN, such as ResNet-UpProj[1]. However, such a learning-based method sometimes estimates unclear or uncertain depth information, especially on the periphery of edges, even if enough learning is done. This paper aims to improve the predicted depth of edge periphery by applying simple image processing based on the position information of edges in an original RGB image to the depth image. Experiments with NYU Depth v2 data-set[2] showed that our simple approach can decrease about 14.3% of the root mean square errors of depth.

Citation Download Citation

Ren Sato and Akio Kimura "A simple refinement for depth information predicted with DNN", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 115152V (1 June 2020); https://doi.org/10.1117/12.2566610

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