Learning depth from monocular sequence with convolutional LSTM network

Chia Hung Yeh*, Yao Pao Huang, Chih Yang Lin, Min Hui Lin

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

1 引文 斯高帕斯(Scopus)

摘要

Resolving depth from monocular RGB image has been a long-standing task in computer vision and robotics. Recently, deep learning based methods has become a popular algorithm on depth estimation. Most existing learning based methods take image-pair as input and utilize feature matching across frames to resolve depth. However, two-frame methods require sufficient and static camera motion to reach optimal performance, while camera motion is usually uncontrollable in most application scenarios. In this paper we propose a recurrent neural network based depth estimation network. With the ability of taking multiple images as input, recurrent neural network will decide by itself which image to reference during estimation. We train a u-net like network architecture which utilizes convolutional LSTM in the encoder. We demonstrate our proposed method with the TUM RGB-D dataset, where our proposed method shows the ability of estimating depth with various sequence lengths as input.

原文英語
主出版物標題Advances in Networked-based Information Systems - The 22nd International Conference on Network-Based Information Systems, NBiS 2019
編輯Leonard Barolli, Hiroaki Nishino, Tomoya Enokido, Makoto Takizawa
發行者Springer Verlag
頁面502-507
頁數6
ISBN(列印)9783030290283
DOIs
出版狀態已發佈 - 2020
事件22nd International Conference on Network-Based Information Systems, NBiS 2019 - Oita, 日本
持續時間: 2019 九月 52019 九月 7

出版系列

名字Advances in Intelligent Systems and Computing
1036
ISSN(列印)2194-5357
ISSN(電子)2194-5365

會議

會議22nd International Conference on Network-Based Information Systems, NBiS 2019
國家/地區日本
城市Oita
期間2019/09/052019/09/07

ASJC Scopus subject areas

  • 控制與系統工程
  • 電腦科學(全部)

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