Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-265-2024
F. Menna, Scott McAvoy, E. Nocerino, B. Tanduo, Louise Giuseffi, A. Calantropio, F. Chiabrando, L. Teppati Losè, A. Lingua, Stuart Sandin, Clinton Edwards, Brian Zgliczynski, D. Rissolo, F. Kuester
Abstract. Underwater photogrammetry presents unique challenges due to the optical properties of water that, if not correctly taken into account, might affect the quality of the survey and the related 2D and 3D products. It is recognized nowadays the importance to train newcomers to underwater surveying, and extend and consolidate the knowledge of best practices for underwater data acquisition. Starting from this consideration, we propose the development of POSER, a 3D simulation framework designed to facilitate the teaching of underwater imaging principles. The project, an ISPRS Educational and Capacity Building Initiative, is built upon the open-source platform Blender, incorporating realistic modelling of the physical properties of water, including light refraction, scattering, and absorption phenomena, to simulate underwater surveying conditions. We foster a learning-by-doing approach, providing users with ready-to-use application scenarios inspired by real-life case studies. They will cover a range of application fields, from marine ecology to archaeology and subsea metrology, and allow users to address the complexities of underwater surveying practices. This paper introduces POSER to the community, presenting its educational vocation and describing its constituent components.
{"title":"POSER: an oPen sOurce Simulation platform for tEaching and tRaining underwater photogrammetry","authors":"F. Menna, Scott McAvoy, E. Nocerino, B. Tanduo, Louise Giuseffi, A. Calantropio, F. Chiabrando, L. Teppati Losè, A. Lingua, Stuart Sandin, Clinton Edwards, Brian Zgliczynski, D. Rissolo, F. Kuester","doi":"10.5194/isprs-archives-xlviii-2-2024-265-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-265-2024","url":null,"abstract":"Abstract. Underwater photogrammetry presents unique challenges due to the optical properties of water that, if not correctly taken into account, might affect the quality of the survey and the related 2D and 3D products. It is recognized nowadays the importance to train newcomers to underwater surveying, and extend and consolidate the knowledge of best practices for underwater data acquisition. Starting from this consideration, we propose the development of POSER, a 3D simulation framework designed to facilitate the teaching of underwater imaging principles. The project, an ISPRS Educational and Capacity Building Initiative, is built upon the open-source platform Blender, incorporating realistic modelling of the physical properties of water, including light refraction, scattering, and absorption phenomena, to simulate underwater surveying conditions. We foster a learning-by-doing approach, providing users with ready-to-use application scenarios inspired by real-life case studies. They will cover a range of application fields, from marine ecology to archaeology and subsea metrology, and allow users to address the complexities of underwater surveying practices. This paper introduces POSER to the community, presenting its educational vocation and describing its constituent components.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"20 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141359303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-225-2024
E. Maset, Andrea Fusiello
Abstract. In recent years, power line inspections have benefited from the use of the lidar surveying technology, which enables safe and rapid data acquisition, even in challenging environments. To further optimize monitoring operations and reduce time and costs, automatic processing of the point clouds obtained is of greatest importance. This work presents a complete pipeline for processing power line data that includes (i) lidar point cloud segmentation using a Fully Convolutional Network, (ii) individual pylon identification via DBSCAN clustering, and (iii) the automatic extraction and modelling of any number of cables using a multi-model fitting algorithm based on the J-Linkage method. The proposed procedure is tested on a 36 km-long power line, resulting in a F1-score of 97.6% for pylons and 98.5% for the vectorized cables.
摘要近年来，激光雷达测量技术的使用使电力线路检测工作受益匪浅，即使在充满挑战的环境中也能安全快速地采集数据。为了进一步优化监测工作，减少时间和成本，对所获得的点云进行自动处理就显得尤为重要。本研究提出了一套完整的电力线数据处理流程，其中包括：(i) 使用全卷积网络进行激光雷达点云分割；(ii) 通过 DBSCAN 聚类进行单个塔架识别；(iii) 使用基于 J-Linkage 方法的多模型拟合算法自动提取任意数量的电缆并为其建模。建议的程序在 36 公里长的电力线上进行了测试，结果塔架的 F1 分数为 97.6%，矢量化电缆的 F1 分数为 98.5%。
{"title":"Automatic Vectorization of Power Lines from Airborne Lidar Point Clouds","authors":"E. Maset, Andrea Fusiello","doi":"10.5194/isprs-archives-xlviii-2-2024-225-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-225-2024","url":null,"abstract":"Abstract. In recent years, power line inspections have benefited from the use of the lidar surveying technology, which enables safe and rapid data acquisition, even in challenging environments. To further optimize monitoring operations and reduce time and costs, automatic processing of the point clouds obtained is of greatest importance. This work presents a complete pipeline for processing power line data that includes (i) lidar point cloud segmentation using a Fully Convolutional Network, (ii) individual pylon identification via DBSCAN clustering, and (iii) the automatic extraction and modelling of any number of cables using a multi-model fitting algorithm based on the J-Linkage method. The proposed procedure is tested on a 36 km-long power line, resulting in a F1-score of 97.6% for pylons and 98.5% for the vectorized cables.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-49-2024
Meida Chen, Devashish Lal, Zifan Yu, Jiuyi Xu, Andrew Feng, Suya You, Abdul Nurunnabi, Yangming Shi
Abstract. The fusion of low-cost unmanned aerial systems (UAS) with advanced photogrammetric techniques has revolutionized 3D terrain reconstruction, enabling the automated creation of detailed models. Concurrently, the advent of 3D Gaussian Splatting has introduced a paradigm shift in 3D data representation, offering visually realistic renditions distinct from traditional polygon-based models. Our research builds upon this foundation, aiming to integrate Gaussian Splatting into interactive simulations for immersive virtual environments. We address challenges such as collision detection by adopting a hybrid approach, combining Gaussian Splatting with photogrammetry-derived meshes. Through comprehensive experimentation covering varying terrain sizes and Gaussian densities, we evaluate scalability, performance, and limitations. Our findings contribute to advancing the use of advanced computer graphics techniques for enhanced 3D terrain visualization and simulation.
{"title":"Large-Scale 3D Terrain Reconstruction Using 3D Gaussian Splatting for Visualization and Simulation","authors":"Meida Chen, Devashish Lal, Zifan Yu, Jiuyi Xu, Andrew Feng, Suya You, Abdul Nurunnabi, Yangming Shi","doi":"10.5194/isprs-archives-xlviii-2-2024-49-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-49-2024","url":null,"abstract":"Abstract. The fusion of low-cost unmanned aerial systems (UAS) with advanced photogrammetric techniques has revolutionized 3D terrain reconstruction, enabling the automated creation of detailed models. Concurrently, the advent of 3D Gaussian Splatting has introduced a paradigm shift in 3D data representation, offering visually realistic renditions distinct from traditional polygon-based models. Our research builds upon this foundation, aiming to integrate Gaussian Splatting into interactive simulations for immersive virtual environments. We address challenges such as collision detection by adopting a hybrid approach, combining Gaussian Splatting with photogrammetry-derived meshes. Through comprehensive experimentation covering varying terrain sizes and Gaussian densities, we evaluate scalability, performance, and limitations. Our findings contribute to advancing the use of advanced computer graphics techniques for enhanced 3D terrain visualization and simulation.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"30 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141357509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-121-2024
P. Helmholz, Tahlia Bassett, Liam Boyle, Nicola Browne, I. Parnum, Molly Moustaka, Richard Evans
Abstract. Corals are critical reef-building organisms, providing essential habitat and ecosystem services. Tracking coral growth over time indicates coral reef health, which can be measured using various established techniques. Several coral growth-related studies have successfully applied photogrammetry to a particular coral of various types. While the focus of previous work was on standardised data processing and, to a certain degree, on the assessment of different point cloud comparison methods (Lange et al. 2022), little attention has been given to the impact of camera calibration. This study measured the annual linear extension of five Acropora spp. colonies using photogrammetry and evaluated all stages of imagery processing. A high focus was given to the analysis of the camera calibration method and the validation of camera parameters derived using an in-situ calibration of coral images with scale bars placed in the camera's field of view. We demonstrate that this method is as reliable as the calibration using a calibration frame. This study also examined the impact of the different point cloud comparison methods for Acropora spp. More specifically, the derived point clouds are compared by applying the point-to-point and point-to-model methods and manually selecting 12 coral branch tips. Histograms derived from the comparison methods were analysed and deemed a suitable and efficient alternative approach for measuring the maximum growth rate of mature colonies over shorter time periods (1 year or less).
{"title":"Evaluating Linear Coral Growth Estimation Using Photogrammetry and Alternative Point Cloud Comparison Methods","authors":"P. Helmholz, Tahlia Bassett, Liam Boyle, Nicola Browne, I. Parnum, Molly Moustaka, Richard Evans","doi":"10.5194/isprs-archives-xlviii-2-2024-121-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-121-2024","url":null,"abstract":"Abstract. Corals are critical reef-building organisms, providing essential habitat and ecosystem services. Tracking coral growth over time indicates coral reef health, which can be measured using various established techniques. Several coral growth-related studies have successfully applied photogrammetry to a particular coral of various types. While the focus of previous work was on standardised data processing and, to a certain degree, on the assessment of different point cloud comparison methods (Lange et al. 2022), little attention has been given to the impact of camera calibration. This study measured the annual linear extension of five Acropora spp. colonies using photogrammetry and evaluated all stages of imagery processing. A high focus was given to the analysis of the camera calibration method and the validation of camera parameters derived using an in-situ calibration of coral images with scale bars placed in the camera's field of view. We demonstrate that this method is as reliable as the calibration using a calibration frame. This study also examined the impact of the different point cloud comparison methods for Acropora spp. More specifically, the derived point clouds are compared by applying the point-to-point and point-to-model methods and manually selecting 12 coral branch tips. Histograms derived from the comparison methods were analysed and deemed a suitable and efficient alternative approach for measuring the maximum growth rate of mature colonies over shorter time periods (1 year or less).\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-257-2024
Qipeng Mei, Janik Steier, D. Iwaszczuk
Abstract. Benefiting from advancements in algorithms and computing capabilities, supervised deep learning models offer significant advantages in accurately mapping individual tree canopy cover, which is a fundamental component of forestry management. In contrast to traditional field measurement methods, deep learning models leveraging remote sensing data circumvent access limitations and are more cost-effective. However, the efficiency of models depends on the accuracy of the tree crown annotations, which are often obtained through manual labeling. The intricate features of the tree crown, characterized by irregular contours, overlapping foliage, and frequent shadowing, pose a challenge for annotators. Therefore, this study explores a novel approach that integrates the annotations of multiple annotators for the same region of interest. It further refines the labels by leveraging information extracted from multi-spectral aerial images. This approach aims to reduce annotation inaccuracies caused by personal preference and bias and obtain a more balanced integrated annotation.
{"title":"Integrating Crowd-sourced Annotations of Tree Crowns using Markov Random Field and Multispectral Information","authors":"Qipeng Mei, Janik Steier, D. Iwaszczuk","doi":"10.5194/isprs-archives-xlviii-2-2024-257-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-257-2024","url":null,"abstract":"Abstract. Benefiting from advancements in algorithms and computing capabilities, supervised deep learning models offer significant advantages in accurately mapping individual tree canopy cover, which is a fundamental component of forestry management. In contrast to traditional field measurement methods, deep learning models leveraging remote sensing data circumvent access limitations and are more cost-effective. However, the efficiency of models depends on the accuracy of the tree crown annotations, which are often obtained through manual labeling. The intricate features of the tree crown, characterized by irregular contours, overlapping foliage, and frequent shadowing, pose a challenge for annotators. Therefore, this study explores a novel approach that integrates the annotations of multiple annotators for the same region of interest. It further refines the labels by leveraging information extracted from multi-spectral aerial images. This approach aims to reduce annotation inaccuracies caused by personal preference and bias and obtain a more balanced integrated annotation.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"51 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141360293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. The registration of full-moon remote sensing images constitutes a pivotal stage in the fusion analysis of multiple lunar remote sensing datasets. Addressing prevailing issues in automatic registration, such as the broad width of full-moon data, significant internal distortion, and texture distortion in high-latitude regions, this paper proposes a method for automatic matching and correction based on triangulation constraints. The approach employs a matching strategy progressing from coarse to fine and from sparse to dense. It optimizes and combines multiple existing matching algorithms, enhances the extraction of initial network points, constructs irregular triangulation networks using these points, conducts dense matching with each triangulation network as a basic unit, and introduces a geometric correction method based on triangulation network + grid (TIN + GRID) for the registration of full-moon data. For the matching of full-moon remote sensing images in high-latitude regions, a novel approach involving memory projection forward transformation-matching-projection inverse transformation is adopted. Through registration experiments with full-moon image data and an analysis of registration accuracy at different latitudes, the average mean square error is found to be less than 2 pixels. These results signify the efficacy of the proposed method in effectively addressing the automatic registration challenges encountered in full-moon remote sensing images.
{"title":"Automated Registration of Full Moon Remote Sensing Images Based on Triangulated Network Constraints","authors":"Huibin Ge, Yu Geng, Xiaojuan Ba, Yuxiang Wang, Jingguo Lv","doi":"10.5194/isprs-archives-xlviii-2-2024-89-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-89-2024","url":null,"abstract":"Abstract. The registration of full-moon remote sensing images constitutes a pivotal stage in the fusion analysis of multiple lunar remote sensing datasets. Addressing prevailing issues in automatic registration, such as the broad width of full-moon data, significant internal distortion, and texture distortion in high-latitude regions, this paper proposes a method for automatic matching and correction based on triangulation constraints. The approach employs a matching strategy progressing from coarse to fine and from sparse to dense. It optimizes and combines multiple existing matching algorithms, enhances the extraction of initial network points, constructs irregular triangulation networks using these points, conducts dense matching with each triangulation network as a basic unit, and introduces a geometric correction method based on triangulation network + grid (TIN + GRID) for the registration of full-moon data. For the matching of full-moon remote sensing images in high-latitude regions, a novel approach involving memory projection forward transformation-matching-projection inverse transformation is adopted. Through registration experiments with full-moon image data and an analysis of registration accuracy at different latitudes, the average mean square error is found to be less than 2 pixels. These results signify the efficacy of the proposed method in effectively addressing the automatic registration challenges encountered in full-moon remote sensing images.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"20 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141357190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-327-2024
G. Pavoni, Jordan Pierce, Clinton B. Edwards, M. Corsini, Vid Petrovic, Paolo Cignoni
Abstract. Large-area image acquisition techniques are essential in underwater investigations: high-resolution 3D image-based reconstructions have improved coral reef monitoring by enabling novel seascape ecological analysis. Artificial intelligence (AI) offers methods for significantly accelerating image data interpretation, such as automatically recognizing, enumerating, and measuring organisms. However, the rapid proliferation of these technological achievements has led to a relative lack of standardization of methods. Remarkably, there are notable differences in procedures for generating human and AI annotations, and there is also a scarcity of publicly available datasets and shared machine-learning models. The lack of standard procedures makes it challenging to compare and reproduce scientific findings. One way to overcome this problem is to make the most used platforms by coral reef scientists interoperable so that the analyses can all be exported into a common format. This paper introduces functionality to promote interoperability between three popular open-source software tools dedicated to the digital study of coral reefs: TagLab, CoralNet, and Viscore. As users of each platform may have different analysis pipelines, we discuss several workflows for managing and processing point and area annotations, improving collaboration among these tools. Our work sets the foundation for a more seamless ecosystem that maintains the established investigation procedures of various laboratories but allows for easier result sharing.
{"title":"Integrating Widespread Coral Reef Monitoring Tools for Managing both Area and Point Annotations","authors":"G. Pavoni, Jordan Pierce, Clinton B. Edwards, M. Corsini, Vid Petrovic, Paolo Cignoni","doi":"10.5194/isprs-archives-xlviii-2-2024-327-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-327-2024","url":null,"abstract":"Abstract. Large-area image acquisition techniques are essential in underwater investigations: high-resolution 3D image-based reconstructions have improved coral reef monitoring by enabling novel seascape ecological analysis. Artificial intelligence (AI) offers methods for significantly accelerating image data interpretation, such as automatically recognizing, enumerating, and measuring organisms. However, the rapid proliferation of these technological achievements has led to a relative lack of standardization of methods. Remarkably, there are notable differences in procedures for generating human and AI annotations, and there is also a scarcity of publicly available datasets and shared machine-learning models. The lack of standard procedures makes it challenging to compare and reproduce scientific findings. One way to overcome this problem is to make the most used platforms by coral reef scientists interoperable so that the analyses can all be exported into a common format. This paper introduces functionality to promote interoperability between three popular open-source software tools dedicated to the digital study of coral reefs: TagLab, CoralNet, and Viscore. As users of each platform may have different analysis pipelines, we discuss several workflows for managing and processing point and area annotations, improving collaboration among these tools. Our work sets the foundation for a more seamless ecosystem that maintains the established investigation procedures of various laboratories but allows for easier result sharing.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141359894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-435-2024
Jinhong Wang, W. Yao
Abstract. Semantic instance segmentation from scenes, serving as a crucial role for 3D modelling and scene understanding. Conducting semantic segmentation before grouping instances is adopted by the existing state-of-the-art methods. However, without additional refinement, semantic errors will fully propagate into the grouping stage, resulting in low overlap with the ground truth instance. Furthermore, the proposed methods focused on indoor level scenes, which are limited when directly applied to large-scale outdoor Airborne Laser Scanning (ALS) point clouds. Numerous instances, significant object density and scale variations make ALS point clouds distinct from indoor data. In order to address the problems, we proposed a geometric characterization-aware semantic instance segmentation network, which utilized both semantic and objectness score to select potential points for grouping. And in point cloud feature learning stage, hand-craft geometry features are taken as input for geometric characterization awareness. Moreover, to address errors propagated from previous modules after grouping, we have additionally designed a per-instance refinement module. To assess semantic instance segmentation, we conducted experiments on an open-source dataset. Additionally, we performed semantic segmentation experiments to evaluate the performance of our proposed point cloud feature learning method.
摘要从场景中进行语义实例分割，对三维建模和场景理解起着至关重要的作用。现有的先进方法都是先进行语义分割，然后再对实例进行分组。然而，如果不进行额外的细化，语义误差将完全扩散到分组阶段，导致与地面实况实例的重叠率较低。此外，所提出的方法侧重于室内水平场景，直接应用于大规模室外机载激光扫描（ALS）点云时受到限制。大量的实例、明显的物体密度和尺度变化使得 ALS 点云与室内数据截然不同。为了解决这些问题，我们提出了一种几何特征感知语义实例分割网络，该网络利用语义和物体度得分来选择潜在的分组点。在点云特征学习阶段，手工制作的几何特征被作为几何特征感知的输入。此外，为了解决分组后先前模块传播的错误，我们还设计了一个按实例细化模块。为了评估语义实例分割，我们在一个开源数据集上进行了实验。此外，我们还进行了语义分割实验，以评估我们提出的点云特征学习方法的性能。
{"title":"An End-to-End Geometric Characterization-aware Semantic Instance Segmentation Network for ALS Point Clouds","authors":"Jinhong Wang, W. Yao","doi":"10.5194/isprs-archives-xlviii-2-2024-435-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-435-2024","url":null,"abstract":"Abstract. Semantic instance segmentation from scenes, serving as a crucial role for 3D modelling and scene understanding. Conducting semantic segmentation before grouping instances is adopted by the existing state-of-the-art methods. However, without additional refinement, semantic errors will fully propagate into the grouping stage, resulting in low overlap with the ground truth instance. Furthermore, the proposed methods focused on indoor level scenes, which are limited when directly applied to large-scale outdoor Airborne Laser Scanning (ALS) point clouds. Numerous instances, significant object density and scale variations make ALS point clouds distinct from indoor data. In order to address the problems, we proposed a geometric characterization-aware semantic instance segmentation network, which utilized both semantic and objectness score to select potential points for grouping. And in point cloud feature learning stage, hand-craft geometry features are taken as input for geometric characterization awareness. Moreover, to address errors propagated from previous modules after grouping, we have additionally designed a per-instance refinement module. To assess semantic instance segmentation, we conducted experiments on an open-source dataset. Additionally, we performed semantic segmentation experiments to evaluate the performance of our proposed point cloud feature learning method.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"39 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141355539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-349-2024
D. Rissolo, Scott McAvoy, Helena Barba Meinecke, H. Moyes, Samuel Meacham, Julien Fortin, Fred Devos, F. Kuester
Abstract. Clearing and construction activities related to the Maya Train (Tren Maya) project resulted in potential and inevitable impacts to archaeological caves sites in largely undeveloped areas of Quintana Roo. An effort coordinated by Mexico’s National Institute of Anthropology and History (INAH) involved accelerated digital documentation of two caves – via SLAM-enabled mobile LiDAR scanning and targeted photogrammetry – to facilitate prompt visualization and evaluation of terrestrial and subterranean geospatial relationships. Mobile LiDAR is well suited to the challenges of capturing the complex, multilevel morphology of caves and was readily deployed across and through priority environments. Specific archaeological features – such as ancient Maya rock art and masonry shrines – were documented via photogrammetry, and the resulting higher-resolution models co-referenced with the georeferenced mobile LiDAR-generated point clouds of each cave and the surrounding topographic context. This integrative approach contributed to a more informed decision-making process, with respect to conservation and construction, and provided baseline data for future monitoring of the affected cave sites.
摘要玛雅列车（Tren Maya）项目的清理和施工活动对金塔纳罗奥州大部分未开发地区的考古洞穴遗址造成了潜在和不可避免的影响。在墨西哥国家人类学与历史研究所（INAH）的协调下，通过支持 SLAM 的移动激光雷达扫描和有针对性的摄影测量，对两个洞穴进行了加速数字记录，以促进对地面和地下地理空间关系的及时可视化和评估。移动激光雷达非常适合应对捕捉洞穴复杂、多层次形态的挑战，并可在优先环境中随时部署。具体的考古特征--如古代玛雅岩画和砖石神龛--通过摄影测量进行记录，并将由此产生的高分辨率模型与地理坐标移动激光雷达生成的每个洞穴的点云和周围地形背景进行共同参照。这种综合方法有助于在保护和建设方面做出更明智的决策，并为今后监测受影响的洞穴遗址提供基准数据。
{"title":"A Multimodal Approach to Rapidly Documenting and Visualizing Archaeological Caves in Quintana Roo, Mexico","authors":"D. Rissolo, Scott McAvoy, Helena Barba Meinecke, H. Moyes, Samuel Meacham, Julien Fortin, Fred Devos, F. Kuester","doi":"10.5194/isprs-archives-xlviii-2-2024-349-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-349-2024","url":null,"abstract":"Abstract. Clearing and construction activities related to the Maya Train (Tren Maya) project resulted in potential and inevitable impacts to archaeological caves sites in largely undeveloped areas of Quintana Roo. An effort coordinated by Mexico’s National Institute of Anthropology and History (INAH) involved accelerated digital documentation of two caves – via SLAM-enabled mobile LiDAR scanning and targeted photogrammetry – to facilitate prompt visualization and evaluation of terrestrial and subterranean geospatial relationships. Mobile LiDAR is well suited to the challenges of capturing the complex, multilevel morphology of caves and was readily deployed across and through priority environments. Specific archaeological features – such as ancient Maya rock art and masonry shrines – were documented via photogrammetry, and the resulting higher-resolution models co-referenced with the georeferenced mobile LiDAR-generated point clouds of each cave and the surrounding topographic context. This integrative approach contributed to a more informed decision-making process, with respect to conservation and construction, and provided baseline data for future monitoring of the affected cave sites.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"37 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141360167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.5194/isprs-archives-xlviii-2-2024-355-2024
R. Rofallski, A. Colson, T. Luhmann
Abstract. This study addresses the challenges inherent in preserving archaeological waterlogged wood, which is prone to deformation and decay if not stabilized immediately after recovery. Conventional preservation methods, such as impregnation with polyethylene glycol (PEG) solutions, often result in undesirable dimensional changes. To obtain exact spatio-temporal information on the deformations during the conservation process, a photogrammetric monitoring system, utilizing a stereo camera facing from air into the liquid, attached to an automated biaxial measurement unit is proposed. Special target heads were developed and attached to the wood to provide deformation points. Refraction correction was applied to the imaging model by ray tracing, and indirect flat lighting was used to mitigate turbidity. The system observed logs from a wooden track from the first century, subject to conservation. Subject of investigation were the influence of refraction negligence and scale definition in a bundle geometry, similar to bathymetric aerial setups. Results show that refraction correction is imperative for good results. Furthermore, scale definition with highly accurately determined scale bars and inclusion of relative orientation constraints provide further accuracy improvements.
{"title":"Multimedia Photogrammetry for Automated 3D Monitoring in Archaeological Waterlogged Wood Conservation","authors":"R. Rofallski, A. Colson, T. Luhmann","doi":"10.5194/isprs-archives-xlviii-2-2024-355-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-355-2024","url":null,"abstract":"Abstract. This study addresses the challenges inherent in preserving archaeological waterlogged wood, which is prone to deformation and decay if not stabilized immediately after recovery. Conventional preservation methods, such as impregnation with polyethylene glycol (PEG) solutions, often result in undesirable dimensional changes. To obtain exact spatio-temporal information on the deformations during the conservation process, a photogrammetric monitoring system, utilizing a stereo camera facing from air into the liquid, attached to an automated biaxial measurement unit is proposed. Special target heads were developed and attached to the wood to provide deformation points. Refraction correction was applied to the imaging model by ray tracing, and indirect flat lighting was used to mitigate turbidity. The system observed logs from a wooden track from the first century, subject to conservation. Subject of investigation were the influence of refraction negligence and scale definition in a bundle geometry, similar to bathymetric aerial setups. Results show that refraction correction is imperative for good results. Furthermore, scale definition with highly accurately determined scale bars and inclusion of relative orientation constraints provide further accuracy improvements.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"44 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141355022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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