Infrared Monocular Depth Estimation Based on Radiation Field Detail Enhancement
Monocular depth estimation (MDE) offers cost-effective approach to 3D perception, while thermal infrared sensors show high robustness under varying illumination conditions. Their combination is crucial for day and night 3D perception, yet it faces two key challenges: 1) inadequate processing of 14-b...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11017611/ |
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| author | Rihua Hao Chao Xu Chonghao Zhong |
| author_facet | Rihua Hao Chao Xu Chonghao Zhong |
| author_sort | Rihua Hao |
| collection | DOAJ |
| description | Monocular depth estimation (MDE) offers cost-effective approach to 3D perception, while thermal infrared sensors show high robustness under varying illumination conditions. Their combination is crucial for day and night 3D perception, yet it faces two key challenges: 1) inadequate processing of 14-bit RAW data, leading to loss of subtle infrared information, and 2) the resolution limitation of current infrared datasets (existing <inline-formula> <tex-math notation="LaTeX">$640\times 512$ </tex-math></inline-formula> sensor collections restrict the camera’s ability to capture critical scene details). To this end, in this paper, we introduce the IRSL dataset, which integrates infrared, stereo RGB, and LiDAR modalities. Data were acquired using a high-resolution thermal infrared camera (<inline-formula> <tex-math notation="LaTeX">$1280\times 1024$ </tex-math></inline-formula> pixels), synchronized stereo cameras, and LiDAR (500K points per frame). The dataset aims to enhance the precision and robustness of depth estimation in both daytime and nighttime environments. Secondly, this paper proposes Radiation Field Detail Enhancement (RFDE), a novel method for enhancing infrared radiation field details in RAW data. This method performs gray mapping based on radiation field intensity and spatial context awareness and uses a bilateral filter to enhance the details in the image. Experimental results show that the proposed RFDE method fully leverages the advantages of high-resolution infrared images, effectively improving the quantitative and qualitative performance of the MDE model (<inline-formula> <tex-math notation="LaTeX">$\delta _{1}$ </tex-math></inline-formula> =0.964, SqRel = 0.441). |
| format | Article |
| id | doaj-art-de84262d2bbe4cc2981edecd15694784 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-de84262d2bbe4cc2981edecd156947842025-08-20T02:34:15ZengIEEEIEEE Access2169-35362025-01-0113977519776410.1109/ACCESS.2025.357493211017611Infrared Monocular Depth Estimation Based on Radiation Field Detail EnhancementRihua Hao0https://orcid.org/0009-0005-2243-3397Chao Xu1https://orcid.org/0000-0002-5696-6301Chonghao Zhong2https://orcid.org/0009-0000-7910-9970Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, School of Optics and Photonics, Beijing Institute of Technology, Beijing, ChinaKey Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, School of Optics and Photonics, Beijing Institute of Technology, Beijing, ChinaKey Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, School of Optics and Photonics, Beijing Institute of Technology, Beijing, ChinaMonocular depth estimation (MDE) offers cost-effective approach to 3D perception, while thermal infrared sensors show high robustness under varying illumination conditions. Their combination is crucial for day and night 3D perception, yet it faces two key challenges: 1) inadequate processing of 14-bit RAW data, leading to loss of subtle infrared information, and 2) the resolution limitation of current infrared datasets (existing <inline-formula> <tex-math notation="LaTeX">$640\times 512$ </tex-math></inline-formula> sensor collections restrict the camera’s ability to capture critical scene details). To this end, in this paper, we introduce the IRSL dataset, which integrates infrared, stereo RGB, and LiDAR modalities. Data were acquired using a high-resolution thermal infrared camera (<inline-formula> <tex-math notation="LaTeX">$1280\times 1024$ </tex-math></inline-formula> pixels), synchronized stereo cameras, and LiDAR (500K points per frame). The dataset aims to enhance the precision and robustness of depth estimation in both daytime and nighttime environments. Secondly, this paper proposes Radiation Field Detail Enhancement (RFDE), a novel method for enhancing infrared radiation field details in RAW data. This method performs gray mapping based on radiation field intensity and spatial context awareness and uses a bilateral filter to enhance the details in the image. Experimental results show that the proposed RFDE method fully leverages the advantages of high-resolution infrared images, effectively improving the quantitative and qualitative performance of the MDE model (<inline-formula> <tex-math notation="LaTeX">$\delta _{1}$ </tex-math></inline-formula> =0.964, SqRel = 0.441).https://ieeexplore.ieee.org/document/11017611/Thermal infrared imagemonocular depth estimationhigh-resolution dataset |
| spellingShingle | Rihua Hao Chao Xu Chonghao Zhong Infrared Monocular Depth Estimation Based on Radiation Field Detail Enhancement IEEE Access Thermal infrared image monocular depth estimation high-resolution dataset |
| title | Infrared Monocular Depth Estimation Based on Radiation Field Detail Enhancement |
| title_full | Infrared Monocular Depth Estimation Based on Radiation Field Detail Enhancement |
| title_fullStr | Infrared Monocular Depth Estimation Based on Radiation Field Detail Enhancement |
| title_full_unstemmed | Infrared Monocular Depth Estimation Based on Radiation Field Detail Enhancement |
| title_short | Infrared Monocular Depth Estimation Based on Radiation Field Detail Enhancement |
| title_sort | infrared monocular depth estimation based on radiation field detail enhancement |
| topic | Thermal infrared image monocular depth estimation high-resolution dataset |
| url | https://ieeexplore.ieee.org/document/11017611/ |
| work_keys_str_mv | AT rihuahao infraredmonoculardepthestimationbasedonradiationfielddetailenhancement AT chaoxu infraredmonoculardepthestimationbasedonradiationfielddetailenhancement AT chonghaozhong infraredmonoculardepthestimationbasedonradiationfielddetailenhancement |