A-Predator: A Multibeam Echosounder Point Cloud Registration Network with Anisotropic Kernel Point Convolution.
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| Title: | A-Predator: A Multibeam Echosounder Point Cloud Registration Network with Anisotropic Kernel Point Convolution. |
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| Authors: | Zhang, Feihu1,2 (AUTHOR), Wang, Penghao1,2 (AUTHOR), Luo, Liguo1 (AUTHOR), Tan, Tingfeng1,2 (AUTHOR), Liu, Fen1 (AUTHOR) liufen0223@mail.nwpu.edu.cn |
| Source: | Remote Sensing. Sep2026, Vol. 18 Issue 17, p3035. 24p. |
| Subjects: | Sonar, Point cloud, Deep learning, Underwater navigation, Feature extraction, Machine learning |
| Abstract: | Highlights: What are the main findings? A novel anisotropic convolution operator (A-KPConv) dynamically adapts kernel shape and orientation to local geometric principal directions, enabling effective feature extraction on strongly anisotropic MBES point clouds. By integrating A-KPConv into Predator, the resulting A-Predator model achieves the highest registration recall among the evaluated methods, improving from 31.63% to 59.55% under extremely low (10%) overlap conditions on the Dotson-east dataset. What are the implications of the main findings? The anisotropic convolution paradigm offers a promising design for point cloud processing tasks involving strip-like or non-uniform data distributions, although its applicability beyond MBES registration remains to be evaluated. Under the evaluated cross-dataset protocol, structure-aware feature learning improves transfer from Dotson-east to the rescaled LQL-MBES data without fine-tuning. Underwater point cloud registration using Multibeam Echosounder (MBES) data is fundamental to marine exploration and seafloor mapping. However, MBES point clouds present unique challenges compared to terrestrial Light Detection and Ranging (LiDAR): high noise levels, low overlap rates, and strongly anisotropic distributions caused by the strip-like sonar scanning pattern. These characteristics degrade existing registration algorithms, which predominantly assume locally isotropic point distributions. To address these challenges, this paper proposes Anisotropic Kernel Point Convolution (A-KPConv), a novel operator tailored to the strip-like structure of MBES point clouds. A-KPConv uses Principal Component Analysis (PCA) to estimate local geometric principal directions and constructs an affine transformation that adapts the convolution kernel shape and orientation to align with the local geometry, thereby shifting feature extraction from isotropic aggregation to structure-aware feature learning along the principal structural directions. Building upon this operator, we integrate A-KPConv into Predator—a framework for low-overlap registration—to develop A-Predator, in which the standard isotropic KPConv in the first three encoder layers is replaced with A-KPConv so that structure-aware feature learning is performed where geometric information is most salient. Extensive experiments on the public Dotson-east dataset and a self-collected LiQuan Lake (LQL) MBES dataset demonstrate that A-Predator achieves the highest registration recall among the evaluated methods. On Dotson-east, recall improves from 31.63% to 59.55% under 10% overlap, with consistently low translation and rotation errors. Ablation studies support the effectiveness of A-KPConv relative to the evaluated anisotropic operators, and cross-dataset experiments suggest more effective transfer than the evaluated baselines from Dotson-east to the rescaled LQL-MBES data without fine-tuning. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A novel anisotropic convolution operator (A-KPConv) dynamically adapts kernel shape and orientation to local geometric principal directions, enabling effective feature extraction on strongly anisotropic MBES point clouds. By integrating A-KPConv into Predator, the resulting A-Predator model achieves the highest registration recall among the evaluated methods, improving from 31.63% to 59.55% under extremely low (10%) overlap conditions on the Dotson-east dataset. What are the implications of the main findings? The anisotropic convolution paradigm offers a promising design for point cloud processing tasks involving strip-like or non-uniform data distributions, although its applicability beyond MBES registration remains to be evaluated. Under the evaluated cross-dataset protocol, structure-aware feature learning improves transfer from Dotson-east to the rescaled LQL-MBES data without fine-tuning. Underwater point cloud registration using Multibeam Echosounder (MBES) data is fundamental to marine exploration and seafloor mapping. However, MBES point clouds present unique challenges compared to terrestrial Light Detection and Ranging (LiDAR): high noise levels, low overlap rates, and strongly anisotropic distributions caused by the strip-like sonar scanning pattern. These characteristics degrade existing registration algorithms, which predominantly assume locally isotropic point distributions. To address these challenges, this paper proposes Anisotropic Kernel Point Convolution (A-KPConv), a novel operator tailored to the strip-like structure of MBES point clouds. A-KPConv uses Principal Component Analysis (PCA) to estimate local geometric principal directions and constructs an affine transformation that adapts the convolution kernel shape and orientation to align with the local geometry, thereby shifting feature extraction from isotropic aggregation to structure-aware feature learning along the principal structural directions. Building upon this operator, we integrate A-KPConv into Predator—a framework for low-overlap registration—to develop A-Predator, in which the standard isotropic KPConv in the first three encoder layers is replaced with A-KPConv so that structure-aware feature learning is performed where geometric information is most salient. Extensive experiments on the public Dotson-east dataset and a self-collected LiQuan Lake (LQL) MBES dataset demonstrate that A-Predator achieves the highest registration recall among the evaluated methods. On Dotson-east, recall improves from 31.63% to 59.55% under 10% overlap, with consistently low translation and rotation errors. Ablation studies support the effectiveness of A-KPConv relative to the evaluated anisotropic operators, and cross-dataset experiments suggest more effective transfer than the evaluated baselines from Dotson-east to the rescaled LQL-MBES data without fine-tuning. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18173035 |
