Complementary depth integration using convolutional autoencoder

Mitsuhiro Murata; Chun Xie; Hidehiko Shishido; Takeshi Endo; Itaru Kitahara

doi:10.1117/12.3018718

2 May 2024 Complementary depth integration using convolutional autoencoder

Mitsuhiro Murata, Chun Xie, Hidehiko Shishido, Takeshi Endo, Itaru Kitahara

Proceedings Volume 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024; 1316421 (2024) https://doi.org/10.1117/12.3018718
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2024, 2024, Langkawi, Malaysia

Abstract

3D spatial recognition is a fundamental technology that supports automatic driving. For example, the processing accuracy of the vehicle depends on the accuracy of depth information around the vehicle body. While methods to geometrically measure the depth of the captured space by applying stereo vision to images taken by multiple cameras become being widely used, it is difficult to measure depth in poorly textured or occluded regions. On the other hand, it becomes possible to estimate depth information in such areas with the advent of estimating depth from monocular images by deep learning. However, if the observation conditions differ between training and estimation, the accuracy of the estimation will decline. This paper proposes a complementary method that integrates both methods by using a convolutional autoencoder.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mitsuhiro Murata, Chun Xie, Hidehiko Shishido, Takeshi Endo, and Itaru Kitahara "Complementary depth integration using convolutional autoencoder", Proc. SPIE 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024, 1316421 (2 May 2024); https://doi.org/10.1117/12.3018718

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KEYWORDS

Deep learning

Education and training

Cameras

Computer programming

Image processing

Image segmentation

LIDAR

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