@inproceedings{d4c162b87fd8464e9abd7066bf9608dd,
title = "Random Mask Slice Stitching(RMSS): A Data Augmentation for Depth Estimation",
abstract = "This work aims to summarize some existing data augmentation methods in the field of depth estimation, and propose a depth estimation data augmentation method suitable for both supervised learning and self-supervised learning. In the training task of computer vision, data augmentation is very significant to improve the accuracy and robustness of the model. However, for depth estimation tasks, data augmentation is not as common as detection or segmentation tasks. Although some data enhancement methods have been proposed in recent years, these data enhancement methods are not effective in different datasets or in supervised learning modes and unsupervised learning modes. Therefore, the data augmentation methods proposed in recent years are sorted out in this paper, mainly for two challenging datasets NYU-Depth-v2 and KITTI. In this paper we propose a simple and effective data augmentation method Random Mask Slice Stitching(RMSS) that can be used in supervised and self-supervised tasks and outperforms existing methods.",
keywords = "Computer vision, Convolutional neural network, Data augmentation, Depth estimation, Transformer",
author = "Ran Li and Zhong Liu and Jie Cao and Xingming Wu and Weihai Chen",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 35th Chinese Control and Decision Conference, CCDC 2023 ; Conference date: 20-05-2023 Through 22-05-2023",
year = "2023",
doi = "10.1109/CCDC58219.2023.10326547",
language = "英语",
series = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "870--875",
booktitle = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",
address = "美国",
}