RockCloud-Align: A High-Precision Benchmark for Rock-Mass Point-Cloud Registration
Rock-mass point-cloud registration is a critical yet challenging task in the fields of geology and engineering. Currently, the lack of dedicated datasets for rock-mass point-cloud registration significantly limits the development and application of advanced algorithms in this area. To address this g...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/345 |
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| author | Yunbiao Wang Dongbo Yu Lupeng Liu Jun Xiao |
| author_facet | Yunbiao Wang Dongbo Yu Lupeng Liu Jun Xiao |
| author_sort | Yunbiao Wang |
| collection | DOAJ |
| description | Rock-mass point-cloud registration is a critical yet challenging task in the fields of geology and engineering. Currently, the lack of dedicated datasets for rock-mass point-cloud registration significantly limits the development and application of advanced algorithms in this area. To address this gap, we introduce RockCloud-Align, a large-scale dataset specifically designed for rock-mass point-cloud registration. Created using high-resolution LiDAR scans, this dataset covers a wide range of geological scenarios with varying densities and includes over 14,000 meticulously curated point-cloud pairs. RockCloud-Align provides a comprehensive benchmark for evaluating registration algorithms, along with a robust evaluation protocol to standardize the assessment of these methods. Building upon this dataset, we propose a novel registration method that eliminates the dependence on feature points and random sampling consensus, ensuring high efficiency and precision across diverse scenes and densities. Extensive experiments demonstrate that the proposed method significantly outperforms existing approaches in both accuracy and computational efficiency. |
| format | Article |
| id | doaj-art-b28f6bbd44754e3faa481a8f38f53994 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-b28f6bbd44754e3faa481a8f38f539942025-01-24T13:48:12ZengMDPI AGRemote Sensing2072-42922025-01-0117234510.3390/rs17020345RockCloud-Align: A High-Precision Benchmark for Rock-Mass Point-Cloud RegistrationYunbiao Wang0Dongbo Yu1Lupeng Liu2Jun Xiao3School of Artificial Intelligence, University of Chinese Academy and Sciences, No. 19 Yuquan Road, Beijing 100049, ChinaSchool of Artificial Intelligence, University of Chinese Academy and Sciences, No. 19 Yuquan Road, Beijing 100049, ChinaSchool of Artificial Intelligence, University of Chinese Academy and Sciences, No. 19 Yuquan Road, Beijing 100049, ChinaSchool of Artificial Intelligence, University of Chinese Academy and Sciences, No. 19 Yuquan Road, Beijing 100049, ChinaRock-mass point-cloud registration is a critical yet challenging task in the fields of geology and engineering. Currently, the lack of dedicated datasets for rock-mass point-cloud registration significantly limits the development and application of advanced algorithms in this area. To address this gap, we introduce RockCloud-Align, a large-scale dataset specifically designed for rock-mass point-cloud registration. Created using high-resolution LiDAR scans, this dataset covers a wide range of geological scenarios with varying densities and includes over 14,000 meticulously curated point-cloud pairs. RockCloud-Align provides a comprehensive benchmark for evaluating registration algorithms, along with a robust evaluation protocol to standardize the assessment of these methods. Building upon this dataset, we propose a novel registration method that eliminates the dependence on feature points and random sampling consensus, ensuring high efficiency and precision across diverse scenes and densities. Extensive experiments demonstrate that the proposed method significantly outperforms existing approaches in both accuracy and computational efficiency.https://www.mdpi.com/2072-4292/17/2/345rock-mass datasetpoint-cloud registrationdeep learningbenchmark |
| spellingShingle | Yunbiao Wang Dongbo Yu Lupeng Liu Jun Xiao RockCloud-Align: A High-Precision Benchmark for Rock-Mass Point-Cloud Registration Remote Sensing rock-mass dataset point-cloud registration deep learning benchmark |
| title | RockCloud-Align: A High-Precision Benchmark for Rock-Mass Point-Cloud Registration |
| title_full | RockCloud-Align: A High-Precision Benchmark for Rock-Mass Point-Cloud Registration |
| title_fullStr | RockCloud-Align: A High-Precision Benchmark for Rock-Mass Point-Cloud Registration |
| title_full_unstemmed | RockCloud-Align: A High-Precision Benchmark for Rock-Mass Point-Cloud Registration |
| title_short | RockCloud-Align: A High-Precision Benchmark for Rock-Mass Point-Cloud Registration |
| title_sort | rockcloud align a high precision benchmark for rock mass point cloud registration |
| topic | rock-mass dataset point-cloud registration deep learning benchmark |
| url | https://www.mdpi.com/2072-4292/17/2/345 |
| work_keys_str_mv | AT yunbiaowang rockcloudalignahighprecisionbenchmarkforrockmasspointcloudregistration AT dongboyu rockcloudalignahighprecisionbenchmarkforrockmasspointcloudregistration AT lupengliu rockcloudalignahighprecisionbenchmarkforrockmasspointcloudregistration AT junxiao rockcloudalignahighprecisionbenchmarkforrockmasspointcloudregistration |