{"title":"雾天条件下的鲁棒密集深度估计","authors":"Hongjin Zhang;Hui Wei;Ren Zheng","doi":"10.1109/TIM.2025.3604923","DOIUrl":null,"url":null,"abstract":"This article addresses the challenge of obtaining reliable dense depth maps from outdoor stereo images captured in foggy weather conditions, which can help vehicles to achieve auto-driving in foggy weather. Traditional methods often fail to account for the impact of fog. Deep learning approaches face difficulties due to the randomness in the type and density of fog, making each fog event unique and training models effectively challenging. To better solve the stereo-matching problem in dense fog conditions, we propose a novel method that leverages preserved information in the matching cost function and neighboring disparity values. By utilizing functional analysis on the matching cost function, we generate candidate disparity results, which are filtered based on neighborhood information. Finally, we decouple the overall energy function to construct univariate functions for each pixel, obtaining the final disparity results through minimization. Experimental evaluations demonstrate the effectiveness of our method in achieving more accurate disparity results in foggy weather.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-18"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robust Dense Depth Estimation in Foggy Weather Conditions\",\"authors\":\"Hongjin Zhang;Hui Wei;Ren Zheng\",\"doi\":\"10.1109/TIM.2025.3604923\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article addresses the challenge of obtaining reliable dense depth maps from outdoor stereo images captured in foggy weather conditions, which can help vehicles to achieve auto-driving in foggy weather. Traditional methods often fail to account for the impact of fog. Deep learning approaches face difficulties due to the randomness in the type and density of fog, making each fog event unique and training models effectively challenging. To better solve the stereo-matching problem in dense fog conditions, we propose a novel method that leverages preserved information in the matching cost function and neighboring disparity values. By utilizing functional analysis on the matching cost function, we generate candidate disparity results, which are filtered based on neighborhood information. Finally, we decouple the overall energy function to construct univariate functions for each pixel, obtaining the final disparity results through minimization. Experimental evaluations demonstrate the effectiveness of our method in achieving more accurate disparity results in foggy weather.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":\"74 \",\"pages\":\"1-18\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Instrumentation and Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11146851/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11146851/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Robust Dense Depth Estimation in Foggy Weather Conditions
This article addresses the challenge of obtaining reliable dense depth maps from outdoor stereo images captured in foggy weather conditions, which can help vehicles to achieve auto-driving in foggy weather. Traditional methods often fail to account for the impact of fog. Deep learning approaches face difficulties due to the randomness in the type and density of fog, making each fog event unique and training models effectively challenging. To better solve the stereo-matching problem in dense fog conditions, we propose a novel method that leverages preserved information in the matching cost function and neighboring disparity values. By utilizing functional analysis on the matching cost function, we generate candidate disparity results, which are filtered based on neighborhood information. Finally, we decouple the overall energy function to construct univariate functions for each pixel, obtaining the final disparity results through minimization. Experimental evaluations demonstrate the effectiveness of our method in achieving more accurate disparity results in foggy weather.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.