GPE-DNeRF:Neural radiance field method for surface geological bodies reconstruction
Three-dimensional (3D) geological models are crucial for a comprehensive understanding of regional geological formations. Deep learning-based 3D reconstruction technologies offer highly automated approaches for recognizing complex data patterns and generating realistic reconstruction results. The ap...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Applied Computing and Geosciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590197425000217 |
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| _version_ | 1849329167854731264 |
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| author | Xinyi Wang Weihua Hua Xiuguo Liu Peng Li Guohe Li |
| author_facet | Xinyi Wang Weihua Hua Xiuguo Liu Peng Li Guohe Li |
| author_sort | Xinyi Wang |
| collection | DOAJ |
| description | Three-dimensional (3D) geological models are crucial for a comprehensive understanding of regional geological formations. Deep learning-based 3D reconstruction technologies offer highly automated approaches for recognizing complex data patterns and generating realistic reconstruction results. The application of these methods in the reconstruction of surface geological bodies is particularly significant in the context of advancing the construction of digital mines nationwide. Neural Radiance Fields (NeRF) have been employed to generate 3D scenes by training models on images captured from different viewpoints. However, parallax errors across viewpoints may lead to misalignment or overlapping of details in the generated images, especially in regions with complex geometric structures. These errors can hinder the model's ability to accurately reconstruct surface details, resulting in substantial distortions in the final output. To address this issue and reduce artifacts and noise in the reconstructed 3D surface geological model, this study explores the use of NeRF for geologic body reconstruction. We propose an enhanced method, GPE-DNeRF, which integrates depth information with Gaussian positional encoding to achieve high-quality reconstruction of geological surfaces. The performance of the proposed method is evaluated, and comparative analyses are conducted with the SfM-MVS and NeRF methods. The GPE-DNeRF method demonstrates a strong capability to eliminate artifacts and retain detailed terrain features, thereby enhancing reconstruction quality and ensuring a closer alignment with actual surface geological conditions. |
| format | Article |
| id | doaj-art-9148c1ff9fbc4faa845fb6ccd498eefb |
| institution | Kabale University |
| issn | 2590-1974 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Computing and Geosciences |
| spelling | doaj-art-9148c1ff9fbc4faa845fb6ccd498eefb2025-08-20T03:47:20ZengElsevierApplied Computing and Geosciences2590-19742025-06-012610023910.1016/j.acags.2025.100239GPE-DNeRF:Neural radiance field method for surface geological bodies reconstructionXinyi Wang0Weihua Hua1Xiuguo Liu2Peng Li3Guohe Li4China University of Geosciences Wuhan, ChinaChina University of Geosciences Wuhan, China; Corresponding author.China University of Geosciences Wuhan, ChinaChina Railway Design Corporation, Tianjin, China; National and Local Joint Engineering Laboratory of Rail Traffic Survey & Design, Tianjin, ChinaChina Railway Design Corporation, Tianjin, China; National and Local Joint Engineering Laboratory of Rail Traffic Survey & Design, Tianjin, ChinaThree-dimensional (3D) geological models are crucial for a comprehensive understanding of regional geological formations. Deep learning-based 3D reconstruction technologies offer highly automated approaches for recognizing complex data patterns and generating realistic reconstruction results. The application of these methods in the reconstruction of surface geological bodies is particularly significant in the context of advancing the construction of digital mines nationwide. Neural Radiance Fields (NeRF) have been employed to generate 3D scenes by training models on images captured from different viewpoints. However, parallax errors across viewpoints may lead to misalignment or overlapping of details in the generated images, especially in regions with complex geometric structures. These errors can hinder the model's ability to accurately reconstruct surface details, resulting in substantial distortions in the final output. To address this issue and reduce artifacts and noise in the reconstructed 3D surface geological model, this study explores the use of NeRF for geologic body reconstruction. We propose an enhanced method, GPE-DNeRF, which integrates depth information with Gaussian positional encoding to achieve high-quality reconstruction of geological surfaces. The performance of the proposed method is evaluated, and comparative analyses are conducted with the SfM-MVS and NeRF methods. The GPE-DNeRF method demonstrates a strong capability to eliminate artifacts and retain detailed terrain features, thereby enhancing reconstruction quality and ensuring a closer alignment with actual surface geological conditions.http://www.sciencedirect.com/science/article/pii/S2590197425000217Reconstruction of surface geological bodiesNeural radiance fieldSurface reconstructionMovement recovery structureGeological image data |
| spellingShingle | Xinyi Wang Weihua Hua Xiuguo Liu Peng Li Guohe Li GPE-DNeRF:Neural radiance field method for surface geological bodies reconstruction Applied Computing and Geosciences Reconstruction of surface geological bodies Neural radiance field Surface reconstruction Movement recovery structure Geological image data |
| title | GPE-DNeRF:Neural radiance field method for surface geological bodies reconstruction |
| title_full | GPE-DNeRF:Neural radiance field method for surface geological bodies reconstruction |
| title_fullStr | GPE-DNeRF:Neural radiance field method for surface geological bodies reconstruction |
| title_full_unstemmed | GPE-DNeRF:Neural radiance field method for surface geological bodies reconstruction |
| title_short | GPE-DNeRF:Neural radiance field method for surface geological bodies reconstruction |
| title_sort | gpe dnerf neural radiance field method for surface geological bodies reconstruction |
| topic | Reconstruction of surface geological bodies Neural radiance field Surface reconstruction Movement recovery structure Geological image data |
| url | http://www.sciencedirect.com/science/article/pii/S2590197425000217 |
| work_keys_str_mv | AT xinyiwang gpednerfneuralradiancefieldmethodforsurfacegeologicalbodiesreconstruction AT weihuahua gpednerfneuralradiancefieldmethodforsurfacegeologicalbodiesreconstruction AT xiuguoliu gpednerfneuralradiancefieldmethodforsurfacegeologicalbodiesreconstruction AT pengli gpednerfneuralradiancefieldmethodforsurfacegeologicalbodiesreconstruction AT guoheli gpednerfneuralradiancefieldmethodforsurfacegeologicalbodiesreconstruction |