{"title":"利用深度估计模型创建不规则边界立体图像数据集","authors":"Muntasser A. Wahsh, Zainab M. Hussain","doi":"10.1556/606.2023.00906","DOIUrl":null,"url":null,"abstract":"Abstract This paper introduces a stereoscopic image and depth dataset created using a deep learning model. It addresses the challenge of obtaining accurate and annotated stereo image pairs with irregular boundaries for deep learning model training. Stereoscopic image and depth dataset provides a unique resource for training deep learning models to handle irregular boundary stereoscopic images, which are valuable for real-world scenarios with complex shapes or occlusions. The dataset is created using monocular depth estimation, a state-of-the-art depth estimation model, and it can be used in applications like rectifying images, estimating depth, detecting objects, and autonomous driving. Overall, this paper presents a novel dataset that demonstrates its effectiveness and potential for advancing stereo vision and developing deep learning models for computer vision applications.","PeriodicalId":35003,"journal":{"name":"Pollack Periodica","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Irregular boundaries stereo images dataset creating using depth estimation model\",\"authors\":\"Muntasser A. Wahsh, Zainab M. Hussain\",\"doi\":\"10.1556/606.2023.00906\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract This paper introduces a stereoscopic image and depth dataset created using a deep learning model. It addresses the challenge of obtaining accurate and annotated stereo image pairs with irregular boundaries for deep learning model training. Stereoscopic image and depth dataset provides a unique resource for training deep learning models to handle irregular boundary stereoscopic images, which are valuable for real-world scenarios with complex shapes or occlusions. The dataset is created using monocular depth estimation, a state-of-the-art depth estimation model, and it can be used in applications like rectifying images, estimating depth, detecting objects, and autonomous driving. Overall, this paper presents a novel dataset that demonstrates its effectiveness and potential for advancing stereo vision and developing deep learning models for computer vision applications.\",\"PeriodicalId\":35003,\"journal\":{\"name\":\"Pollack Periodica\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pollack Periodica\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1556/606.2023.00906\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pollack Periodica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1556/606.2023.00906","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
Irregular boundaries stereo images dataset creating using depth estimation model
Abstract This paper introduces a stereoscopic image and depth dataset created using a deep learning model. It addresses the challenge of obtaining accurate and annotated stereo image pairs with irregular boundaries for deep learning model training. Stereoscopic image and depth dataset provides a unique resource for training deep learning models to handle irregular boundary stereoscopic images, which are valuable for real-world scenarios with complex shapes or occlusions. The dataset is created using monocular depth estimation, a state-of-the-art depth estimation model, and it can be used in applications like rectifying images, estimating depth, detecting objects, and autonomous driving. Overall, this paper presents a novel dataset that demonstrates its effectiveness and potential for advancing stereo vision and developing deep learning models for computer vision applications.
期刊介绍:
Pollack Periodica is an interdisciplinary, peer-reviewed journal that provides an international forum for the presentation, discussion and dissemination of the latest advances and developments in engineering and informatics. Pollack Periodica invites papers reporting new research and applications from a wide range of discipline, including civil, mechanical, electrical, environmental, earthquake, material and information engineering. The journal aims at reaching a wider audience, not only researchers, but also those likely to be most affected by research results, for example designers, fabricators, specialists, developers, computer scientists managers in academic, governmental and industrial communities.