Neural Radiation Fields in a Tidal Flat Environment

Tidal flats are critical ecosystems, playing a vital role in biodiversity conservation and ecological balance. Collecting tidal flat environmental information using unmanned aerial vehicles (UAVs) and subsequently utilizing 3D reconstruction techniques for their detection and protection holds significance in providing comprehensive and detailed tidal flat information, including terrain, slope, and other parameters. It also enables scientific decision-making for the preservation of tidal flat ecosystems and the monitoring of factors such as rising sea levels. Moreover, the latest advancements in neural radiance fields (Nerf) have provided valuable insights and novel perspectives for our work. We face the following challenges: (1) the performance of a single network is limited due to the vast area to cover; (2) regions far from the camera center may exhibit suboptimal rendering results; and (3) changes in lighting conditions present challenges for the achievement of precise reconstruction. To tackle these challenges, we partitioned the tidal flat scene into distinct submodules, carefully preserving overlapping regions between each submodule for collaborative optimization. The luminance of each image is quantified by the appearance embedding vector produced by every captured image. Subsequently, this corresponding vector serves as an input to the model, enhancing its performance across varying lighting conditions. We also introduce an ellipsoidal sphere transformation that brings distant image elements into the sphere’s interior, enhancing the algorithm’s capacity to represent remote image information. Our algorithm is validated using tidal plane images collected from UAVs and compared with traditional Nerf based on two metrics: peak signal-to-noise ratio (PSNR) and learned perceptual image patch similarity (LPIPS). Our method enhances the PSNR value by 2.28 and reduces the LPIPS value by 0.11. The results further demonstrate that our approach significantly enhances Nerf’s performance in tidal flat environments. Utilizing Nerf for the 3D reconstruction of tidal flats, we bypass the need for explicit representation and geometric priors. This innovative approach yields superior novel view synthesis and enhances geometric perception, resulting in high-quality reconstructions. Our method not only provides valuable data but also offers profound insights for environmental monitoring and management. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Huilin Ge [verfasserIn] Zhiyu Zhu [verfasserIn] Haiyang Qiu [verfasserIn] Youwen Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	tidal flats neural radiation fields 3D reconstruction image processing

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 13(2023), 10848, p 10848
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:10848, p 10848

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app131910848

Katalog-ID:	DOAJ093244223

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allfields	10.3390/app131910848 doi (DE-627)DOAJ093244223 (DE-599)DOAJ12b41c9b15094ca88649ffd63827c59c DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Huilin Ge verfasserin aut Neural Radiation Fields in a Tidal Flat Environment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tidal flats are critical ecosystems, playing a vital role in biodiversity conservation and ecological balance. Collecting tidal flat environmental information using unmanned aerial vehicles (UAVs) and subsequently utilizing 3D reconstruction techniques for their detection and protection holds significance in providing comprehensive and detailed tidal flat information, including terrain, slope, and other parameters. It also enables scientific decision-making for the preservation of tidal flat ecosystems and the monitoring of factors such as rising sea levels. Moreover, the latest advancements in neural radiance fields (Nerf) have provided valuable insights and novel perspectives for our work. We face the following challenges: (1) the performance of a single network is limited due to the vast area to cover; (2) regions far from the camera center may exhibit suboptimal rendering results; and (3) changes in lighting conditions present challenges for the achievement of precise reconstruction. To tackle these challenges, we partitioned the tidal flat scene into distinct submodules, carefully preserving overlapping regions between each submodule for collaborative optimization. The luminance of each image is quantified by the appearance embedding vector produced by every captured image. Subsequently, this corresponding vector serves as an input to the model, enhancing its performance across varying lighting conditions. We also introduce an ellipsoidal sphere transformation that brings distant image elements into the sphere’s interior, enhancing the algorithm’s capacity to represent remote image information. Our algorithm is validated using tidal plane images collected from UAVs and compared with traditional Nerf based on two metrics: peak signal-to-noise ratio (PSNR) and learned perceptual image patch similarity (LPIPS). Our method enhances the PSNR value by 2.28 and reduces the LPIPS value by 0.11. The results further demonstrate that our approach significantly enhances Nerf’s performance in tidal flat environments. Utilizing Nerf for the 3D reconstruction of tidal flats, we bypass the need for explicit representation and geometric priors. This innovative approach yields superior novel view synthesis and enhances geometric perception, resulting in high-quality reconstructions. Our method not only provides valuable data but also offers profound insights for environmental monitoring and management. tidal flats neural radiation fields 3D reconstruction image processing Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Zhiyu Zhu verfasserin aut Haiyang Qiu verfasserin aut Youwen Zhang verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 10848, p 10848 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:10848, p 10848 https://doi.org/10.3390/app131910848 kostenfrei https://doaj.org/article/12b41c9b15094ca88649ffd63827c59c kostenfrei https://www.mdpi.com/2076-3417/13/19/10848 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 10848, p 10848
spelling	10.3390/app131910848 doi (DE-627)DOAJ093244223 (DE-599)DOAJ12b41c9b15094ca88649ffd63827c59c DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Huilin Ge verfasserin aut Neural Radiation Fields in a Tidal Flat Environment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tidal flats are critical ecosystems, playing a vital role in biodiversity conservation and ecological balance. Collecting tidal flat environmental information using unmanned aerial vehicles (UAVs) and subsequently utilizing 3D reconstruction techniques for their detection and protection holds significance in providing comprehensive and detailed tidal flat information, including terrain, slope, and other parameters. It also enables scientific decision-making for the preservation of tidal flat ecosystems and the monitoring of factors such as rising sea levels. Moreover, the latest advancements in neural radiance fields (Nerf) have provided valuable insights and novel perspectives for our work. We face the following challenges: (1) the performance of a single network is limited due to the vast area to cover; (2) regions far from the camera center may exhibit suboptimal rendering results; and (3) changes in lighting conditions present challenges for the achievement of precise reconstruction. To tackle these challenges, we partitioned the tidal flat scene into distinct submodules, carefully preserving overlapping regions between each submodule for collaborative optimization. The luminance of each image is quantified by the appearance embedding vector produced by every captured image. Subsequently, this corresponding vector serves as an input to the model, enhancing its performance across varying lighting conditions. We also introduce an ellipsoidal sphere transformation that brings distant image elements into the sphere’s interior, enhancing the algorithm’s capacity to represent remote image information. Our algorithm is validated using tidal plane images collected from UAVs and compared with traditional Nerf based on two metrics: peak signal-to-noise ratio (PSNR) and learned perceptual image patch similarity (LPIPS). Our method enhances the PSNR value by 2.28 and reduces the LPIPS value by 0.11. The results further demonstrate that our approach significantly enhances Nerf’s performance in tidal flat environments. Utilizing Nerf for the 3D reconstruction of tidal flats, we bypass the need for explicit representation and geometric priors. This innovative approach yields superior novel view synthesis and enhances geometric perception, resulting in high-quality reconstructions. Our method not only provides valuable data but also offers profound insights for environmental monitoring and management. tidal flats neural radiation fields 3D reconstruction image processing Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Zhiyu Zhu verfasserin aut Haiyang Qiu verfasserin aut Youwen Zhang verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 10848, p 10848 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:10848, p 10848 https://doi.org/10.3390/app131910848 kostenfrei https://doaj.org/article/12b41c9b15094ca88649ffd63827c59c kostenfrei https://www.mdpi.com/2076-3417/13/19/10848 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 10848, p 10848
allfields_unstemmed	10.3390/app131910848 doi (DE-627)DOAJ093244223 (DE-599)DOAJ12b41c9b15094ca88649ffd63827c59c DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Huilin Ge verfasserin aut Neural Radiation Fields in a Tidal Flat Environment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tidal flats are critical ecosystems, playing a vital role in biodiversity conservation and ecological balance. Collecting tidal flat environmental information using unmanned aerial vehicles (UAVs) and subsequently utilizing 3D reconstruction techniques for their detection and protection holds significance in providing comprehensive and detailed tidal flat information, including terrain, slope, and other parameters. It also enables scientific decision-making for the preservation of tidal flat ecosystems and the monitoring of factors such as rising sea levels. Moreover, the latest advancements in neural radiance fields (Nerf) have provided valuable insights and novel perspectives for our work. We face the following challenges: (1) the performance of a single network is limited due to the vast area to cover; (2) regions far from the camera center may exhibit suboptimal rendering results; and (3) changes in lighting conditions present challenges for the achievement of precise reconstruction. To tackle these challenges, we partitioned the tidal flat scene into distinct submodules, carefully preserving overlapping regions between each submodule for collaborative optimization. The luminance of each image is quantified by the appearance embedding vector produced by every captured image. Subsequently, this corresponding vector serves as an input to the model, enhancing its performance across varying lighting conditions. We also introduce an ellipsoidal sphere transformation that brings distant image elements into the sphere’s interior, enhancing the algorithm’s capacity to represent remote image information. Our algorithm is validated using tidal plane images collected from UAVs and compared with traditional Nerf based on two metrics: peak signal-to-noise ratio (PSNR) and learned perceptual image patch similarity (LPIPS). Our method enhances the PSNR value by 2.28 and reduces the LPIPS value by 0.11. The results further demonstrate that our approach significantly enhances Nerf’s performance in tidal flat environments. Utilizing Nerf for the 3D reconstruction of tidal flats, we bypass the need for explicit representation and geometric priors. This innovative approach yields superior novel view synthesis and enhances geometric perception, resulting in high-quality reconstructions. Our method not only provides valuable data but also offers profound insights for environmental monitoring and management. tidal flats neural radiation fields 3D reconstruction image processing Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Zhiyu Zhu verfasserin aut Haiyang Qiu verfasserin aut Youwen Zhang verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 10848, p 10848 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:10848, p 10848 https://doi.org/10.3390/app131910848 kostenfrei https://doaj.org/article/12b41c9b15094ca88649ffd63827c59c kostenfrei https://www.mdpi.com/2076-3417/13/19/10848 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 10848, p 10848
allfieldsGer	10.3390/app131910848 doi (DE-627)DOAJ093244223 (DE-599)DOAJ12b41c9b15094ca88649ffd63827c59c DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Huilin Ge verfasserin aut Neural Radiation Fields in a Tidal Flat Environment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tidal flats are critical ecosystems, playing a vital role in biodiversity conservation and ecological balance. Collecting tidal flat environmental information using unmanned aerial vehicles (UAVs) and subsequently utilizing 3D reconstruction techniques for their detection and protection holds significance in providing comprehensive and detailed tidal flat information, including terrain, slope, and other parameters. It also enables scientific decision-making for the preservation of tidal flat ecosystems and the monitoring of factors such as rising sea levels. Moreover, the latest advancements in neural radiance fields (Nerf) have provided valuable insights and novel perspectives for our work. We face the following challenges: (1) the performance of a single network is limited due to the vast area to cover; (2) regions far from the camera center may exhibit suboptimal rendering results; and (3) changes in lighting conditions present challenges for the achievement of precise reconstruction. To tackle these challenges, we partitioned the tidal flat scene into distinct submodules, carefully preserving overlapping regions between each submodule for collaborative optimization. The luminance of each image is quantified by the appearance embedding vector produced by every captured image. Subsequently, this corresponding vector serves as an input to the model, enhancing its performance across varying lighting conditions. We also introduce an ellipsoidal sphere transformation that brings distant image elements into the sphere’s interior, enhancing the algorithm’s capacity to represent remote image information. Our algorithm is validated using tidal plane images collected from UAVs and compared with traditional Nerf based on two metrics: peak signal-to-noise ratio (PSNR) and learned perceptual image patch similarity (LPIPS). Our method enhances the PSNR value by 2.28 and reduces the LPIPS value by 0.11. The results further demonstrate that our approach significantly enhances Nerf’s performance in tidal flat environments. Utilizing Nerf for the 3D reconstruction of tidal flats, we bypass the need for explicit representation and geometric priors. This innovative approach yields superior novel view synthesis and enhances geometric perception, resulting in high-quality reconstructions. Our method not only provides valuable data but also offers profound insights for environmental monitoring and management. tidal flats neural radiation fields 3D reconstruction image processing Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Zhiyu Zhu verfasserin aut Haiyang Qiu verfasserin aut Youwen Zhang verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 10848, p 10848 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:10848, p 10848 https://doi.org/10.3390/app131910848 kostenfrei https://doaj.org/article/12b41c9b15094ca88649ffd63827c59c kostenfrei https://www.mdpi.com/2076-3417/13/19/10848 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 10848, p 10848
allfieldsSound	10.3390/app131910848 doi (DE-627)DOAJ093244223 (DE-599)DOAJ12b41c9b15094ca88649ffd63827c59c DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Huilin Ge verfasserin aut Neural Radiation Fields in a Tidal Flat Environment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tidal flats are critical ecosystems, playing a vital role in biodiversity conservation and ecological balance. Collecting tidal flat environmental information using unmanned aerial vehicles (UAVs) and subsequently utilizing 3D reconstruction techniques for their detection and protection holds significance in providing comprehensive and detailed tidal flat information, including terrain, slope, and other parameters. It also enables scientific decision-making for the preservation of tidal flat ecosystems and the monitoring of factors such as rising sea levels. Moreover, the latest advancements in neural radiance fields (Nerf) have provided valuable insights and novel perspectives for our work. We face the following challenges: (1) the performance of a single network is limited due to the vast area to cover; (2) regions far from the camera center may exhibit suboptimal rendering results; and (3) changes in lighting conditions present challenges for the achievement of precise reconstruction. To tackle these challenges, we partitioned the tidal flat scene into distinct submodules, carefully preserving overlapping regions between each submodule for collaborative optimization. The luminance of each image is quantified by the appearance embedding vector produced by every captured image. Subsequently, this corresponding vector serves as an input to the model, enhancing its performance across varying lighting conditions. We also introduce an ellipsoidal sphere transformation that brings distant image elements into the sphere’s interior, enhancing the algorithm’s capacity to represent remote image information. Our algorithm is validated using tidal plane images collected from UAVs and compared with traditional Nerf based on two metrics: peak signal-to-noise ratio (PSNR) and learned perceptual image patch similarity (LPIPS). Our method enhances the PSNR value by 2.28 and reduces the LPIPS value by 0.11. The results further demonstrate that our approach significantly enhances Nerf’s performance in tidal flat environments. Utilizing Nerf for the 3D reconstruction of tidal flats, we bypass the need for explicit representation and geometric priors. This innovative approach yields superior novel view synthesis and enhances geometric perception, resulting in high-quality reconstructions. Our method not only provides valuable data but also offers profound insights for environmental monitoring and management. tidal flats neural radiation fields 3D reconstruction image processing Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Zhiyu Zhu verfasserin aut Haiyang Qiu verfasserin aut Youwen Zhang verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 10848, p 10848 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:10848, p 10848 https://doi.org/10.3390/app131910848 kostenfrei https://doaj.org/article/12b41c9b15094ca88649ffd63827c59c kostenfrei https://www.mdpi.com/2076-3417/13/19/10848 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 10848, p 10848
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source	In Applied Sciences 13(2023), 10848, p 10848 volume:13 year:2023 number:10848, p 10848
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authorswithroles_txt_mv	Huilin Ge @@aut@@ Zhiyu Zhu @@aut@@ Haiyang Qiu @@aut@@ Youwen Zhang @@aut@@
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author	Huilin Ge
spellingShingle	Huilin Ge misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc tidal flats misc neural radiation fields misc 3D reconstruction misc image processing misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Neural Radiation Fields in a Tidal Flat Environment
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 Neural Radiation Fields in a Tidal Flat Environment tidal flats neural radiation fields 3D reconstruction image processing
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc tidal flats misc neural radiation fields misc 3D reconstruction misc image processing misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
topic_unstemmed	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc tidal flats misc neural radiation fields misc 3D reconstruction misc image processing misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
topic_browse	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc tidal flats misc neural radiation fields misc 3D reconstruction misc image processing misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	Neural Radiation Fields in a Tidal Flat Environment
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title_full	Neural Radiation Fields in a Tidal Flat Environment
author_sort	Huilin Ge
journal	Applied Sciences
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author_browse	Huilin Ge Zhiyu Zhu Haiyang Qiu Youwen Zhang
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author-letter	Huilin Ge
doi_str_mv	10.3390/app131910848
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title_sort	neural radiation fields in a tidal flat environment
callnumber	TA1-2040
title_auth	Neural Radiation Fields in a Tidal Flat Environment
abstract	Tidal flats are critical ecosystems, playing a vital role in biodiversity conservation and ecological balance. Collecting tidal flat environmental information using unmanned aerial vehicles (UAVs) and subsequently utilizing 3D reconstruction techniques for their detection and protection holds significance in providing comprehensive and detailed tidal flat information, including terrain, slope, and other parameters. It also enables scientific decision-making for the preservation of tidal flat ecosystems and the monitoring of factors such as rising sea levels. Moreover, the latest advancements in neural radiance fields (Nerf) have provided valuable insights and novel perspectives for our work. We face the following challenges: (1) the performance of a single network is limited due to the vast area to cover; (2) regions far from the camera center may exhibit suboptimal rendering results; and (3) changes in lighting conditions present challenges for the achievement of precise reconstruction. To tackle these challenges, we partitioned the tidal flat scene into distinct submodules, carefully preserving overlapping regions between each submodule for collaborative optimization. The luminance of each image is quantified by the appearance embedding vector produced by every captured image. Subsequently, this corresponding vector serves as an input to the model, enhancing its performance across varying lighting conditions. We also introduce an ellipsoidal sphere transformation that brings distant image elements into the sphere’s interior, enhancing the algorithm’s capacity to represent remote image information. Our algorithm is validated using tidal plane images collected from UAVs and compared with traditional Nerf based on two metrics: peak signal-to-noise ratio (PSNR) and learned perceptual image patch similarity (LPIPS). Our method enhances the PSNR value by 2.28 and reduces the LPIPS value by 0.11. The results further demonstrate that our approach significantly enhances Nerf’s performance in tidal flat environments. Utilizing Nerf for the 3D reconstruction of tidal flats, we bypass the need for explicit representation and geometric priors. This innovative approach yields superior novel view synthesis and enhances geometric perception, resulting in high-quality reconstructions. Our method not only provides valuable data but also offers profound insights for environmental monitoring and management.
abstractGer	Tidal flats are critical ecosystems, playing a vital role in biodiversity conservation and ecological balance. Collecting tidal flat environmental information using unmanned aerial vehicles (UAVs) and subsequently utilizing 3D reconstruction techniques for their detection and protection holds significance in providing comprehensive and detailed tidal flat information, including terrain, slope, and other parameters. It also enables scientific decision-making for the preservation of tidal flat ecosystems and the monitoring of factors such as rising sea levels. Moreover, the latest advancements in neural radiance fields (Nerf) have provided valuable insights and novel perspectives for our work. We face the following challenges: (1) the performance of a single network is limited due to the vast area to cover; (2) regions far from the camera center may exhibit suboptimal rendering results; and (3) changes in lighting conditions present challenges for the achievement of precise reconstruction. To tackle these challenges, we partitioned the tidal flat scene into distinct submodules, carefully preserving overlapping regions between each submodule for collaborative optimization. The luminance of each image is quantified by the appearance embedding vector produced by every captured image. Subsequently, this corresponding vector serves as an input to the model, enhancing its performance across varying lighting conditions. We also introduce an ellipsoidal sphere transformation that brings distant image elements into the sphere’s interior, enhancing the algorithm’s capacity to represent remote image information. Our algorithm is validated using tidal plane images collected from UAVs and compared with traditional Nerf based on two metrics: peak signal-to-noise ratio (PSNR) and learned perceptual image patch similarity (LPIPS). Our method enhances the PSNR value by 2.28 and reduces the LPIPS value by 0.11. The results further demonstrate that our approach significantly enhances Nerf’s performance in tidal flat environments. Utilizing Nerf for the 3D reconstruction of tidal flats, we bypass the need for explicit representation and geometric priors. This innovative approach yields superior novel view synthesis and enhances geometric perception, resulting in high-quality reconstructions. Our method not only provides valuable data but also offers profound insights for environmental monitoring and management.
abstract_unstemmed	Tidal flats are critical ecosystems, playing a vital role in biodiversity conservation and ecological balance. Collecting tidal flat environmental information using unmanned aerial vehicles (UAVs) and subsequently utilizing 3D reconstruction techniques for their detection and protection holds significance in providing comprehensive and detailed tidal flat information, including terrain, slope, and other parameters. It also enables scientific decision-making for the preservation of tidal flat ecosystems and the monitoring of factors such as rising sea levels. Moreover, the latest advancements in neural radiance fields (Nerf) have provided valuable insights and novel perspectives for our work. We face the following challenges: (1) the performance of a single network is limited due to the vast area to cover; (2) regions far from the camera center may exhibit suboptimal rendering results; and (3) changes in lighting conditions present challenges for the achievement of precise reconstruction. To tackle these challenges, we partitioned the tidal flat scene into distinct submodules, carefully preserving overlapping regions between each submodule for collaborative optimization. The luminance of each image is quantified by the appearance embedding vector produced by every captured image. Subsequently, this corresponding vector serves as an input to the model, enhancing its performance across varying lighting conditions. We also introduce an ellipsoidal sphere transformation that brings distant image elements into the sphere’s interior, enhancing the algorithm’s capacity to represent remote image information. Our algorithm is validated using tidal plane images collected from UAVs and compared with traditional Nerf based on two metrics: peak signal-to-noise ratio (PSNR) and learned perceptual image patch similarity (LPIPS). Our method enhances the PSNR value by 2.28 and reduces the LPIPS value by 0.11. The results further demonstrate that our approach significantly enhances Nerf’s performance in tidal flat environments. Utilizing Nerf for the 3D reconstruction of tidal flats, we bypass the need for explicit representation and geometric priors. This innovative approach yields superior novel view synthesis and enhances geometric perception, resulting in high-quality reconstructions. Our method not only provides valuable data but also offers profound insights for environmental monitoring and management.
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title_short	Neural Radiation Fields in a Tidal Flat Environment
url	https://doi.org/10.3390/app131910848 https://doaj.org/article/12b41c9b15094ca88649ffd63827c59c https://www.mdpi.com/2076-3417/13/19/10848 https://doaj.org/toc/2076-3417
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author2	Zhiyu Zhu Haiyang Qiu Youwen Zhang
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