Haitao Luo, Jinming Zhang, Xiongfei Liu, Lili Zhang, Junyi Liu
{"title":"根据多视角图像进行大规模三维重建:全面回顾","authors":"Haitao Luo, Jinming Zhang, Xiongfei Liu, Lili Zhang, Junyi Liu","doi":"10.3390/rs16050773","DOIUrl":null,"url":null,"abstract":"Three-dimensional reconstruction is a key technology employed to represent virtual reality in the real world, which is valuable in computer vision. Large-scale 3D models have broad application prospects in the fields of smart cities, navigation, virtual tourism, disaster warning, and search-and-rescue missions. Unfortunately, most image-based studies currently prioritize the speed and accuracy of 3D reconstruction in indoor scenes. While there are some studies that address large-scale scenes, there has been a lack of systematic comprehensive efforts to bring together the advancements made in the field of 3D reconstruction in large-scale scenes. Hence, this paper presents a comprehensive overview of a 3D reconstruction technique that utilizes multi-view imagery from large-scale scenes. In this article, a comprehensive summary and analysis of vision-based 3D reconstruction technology for large-scale scenes are presented. The 3D reconstruction algorithms are extensively categorized into traditional and learning-based methods. Furthermore, these methods can be categorized based on whether the sensor actively illuminates objects with light sources, resulting in two categories: active and passive methods. Two active methods, namely, structured light and laser scanning, are briefly introduced. The focus then shifts to structure from motion (SfM), stereo matching, and multi-view stereo (MVS), encompassing both traditional and learning-based approaches. Additionally, a novel approach of neural-radiance-field-based 3D reconstruction is introduced. The workflow and improvements in large-scale scenes are elaborated upon. Subsequently, some well-known datasets and evaluation metrics for various 3D reconstruction tasks are introduced. Lastly, a summary of the challenges encountered in the application of 3D reconstruction technology in large-scale outdoor scenes is provided, along with predictions for future trends in development.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Large-Scale 3D Reconstruction from Multi-View Imagery: A Comprehensive Review\",\"authors\":\"Haitao Luo, Jinming Zhang, Xiongfei Liu, Lili Zhang, Junyi Liu\",\"doi\":\"10.3390/rs16050773\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Three-dimensional reconstruction is a key technology employed to represent virtual reality in the real world, which is valuable in computer vision. Large-scale 3D models have broad application prospects in the fields of smart cities, navigation, virtual tourism, disaster warning, and search-and-rescue missions. Unfortunately, most image-based studies currently prioritize the speed and accuracy of 3D reconstruction in indoor scenes. While there are some studies that address large-scale scenes, there has been a lack of systematic comprehensive efforts to bring together the advancements made in the field of 3D reconstruction in large-scale scenes. Hence, this paper presents a comprehensive overview of a 3D reconstruction technique that utilizes multi-view imagery from large-scale scenes. In this article, a comprehensive summary and analysis of vision-based 3D reconstruction technology for large-scale scenes are presented. The 3D reconstruction algorithms are extensively categorized into traditional and learning-based methods. Furthermore, these methods can be categorized based on whether the sensor actively illuminates objects with light sources, resulting in two categories: active and passive methods. Two active methods, namely, structured light and laser scanning, are briefly introduced. The focus then shifts to structure from motion (SfM), stereo matching, and multi-view stereo (MVS), encompassing both traditional and learning-based approaches. Additionally, a novel approach of neural-radiance-field-based 3D reconstruction is introduced. The workflow and improvements in large-scale scenes are elaborated upon. Subsequently, some well-known datasets and evaluation metrics for various 3D reconstruction tasks are introduced. Lastly, a summary of the challenges encountered in the application of 3D reconstruction technology in large-scale outdoor scenes is provided, along with predictions for future trends in development.\",\"PeriodicalId\":20944,\"journal\":{\"name\":\"Remote. 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Large-Scale 3D Reconstruction from Multi-View Imagery: A Comprehensive Review
Three-dimensional reconstruction is a key technology employed to represent virtual reality in the real world, which is valuable in computer vision. Large-scale 3D models have broad application prospects in the fields of smart cities, navigation, virtual tourism, disaster warning, and search-and-rescue missions. Unfortunately, most image-based studies currently prioritize the speed and accuracy of 3D reconstruction in indoor scenes. While there are some studies that address large-scale scenes, there has been a lack of systematic comprehensive efforts to bring together the advancements made in the field of 3D reconstruction in large-scale scenes. Hence, this paper presents a comprehensive overview of a 3D reconstruction technique that utilizes multi-view imagery from large-scale scenes. In this article, a comprehensive summary and analysis of vision-based 3D reconstruction technology for large-scale scenes are presented. The 3D reconstruction algorithms are extensively categorized into traditional and learning-based methods. Furthermore, these methods can be categorized based on whether the sensor actively illuminates objects with light sources, resulting in two categories: active and passive methods. Two active methods, namely, structured light and laser scanning, are briefly introduced. The focus then shifts to structure from motion (SfM), stereo matching, and multi-view stereo (MVS), encompassing both traditional and learning-based approaches. Additionally, a novel approach of neural-radiance-field-based 3D reconstruction is introduced. The workflow and improvements in large-scale scenes are elaborated upon. Subsequently, some well-known datasets and evaluation metrics for various 3D reconstruction tasks are introduced. Lastly, a summary of the challenges encountered in the application of 3D reconstruction technology in large-scale outdoor scenes is provided, along with predictions for future trends in development.