Energy Optimization-Based Segmentation and Extraction of 3D Sonar Point Clouds for Complex Underwater Structures.
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| Title: | Energy Optimization-Based Segmentation and Extraction of 3D Sonar Point Clouds for Complex Underwater Structures. |
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| Authors: | Dong, Junchi1 (AUTHOR), Li, Zilong1,2 (AUTHOR), Li, Hao1 (AUTHOR), Li, Shaobo1,2 (AUTHOR), Wu, Yunlong1,2 (AUTHOR) wuyunlong@cug.edu.cn |
| Source: | Remote Sensing. Aug2026, Vol. 18 Issue 16, p2759. 28p. |
| Subjects: | Point cloud, Image segmentation, Sonar imaging, Noise control, Submerged structures, Mathematical optimization |
| Abstract: | Highlights: What are the main findings? Global Energy Optimization Framework: A novel object-based energy optimization segmentation model is proposed, enabling the simultaneous and precise extraction of both large-area conspicuous planes and small-scale inconspicuous structural features from 3D sonar point clouds. Integrated Denoising and Complexity Reduction: By combining VCCS supervoxel clustering with a multi-attribute-constrained region-growing algorithm, the method significantly reduces computational costs and effectively filters typical sonar noise artifacts, such as scattering and multipath effects. What are the implications of the main findings? Enhanced Underwater Monitoring Accuracy: Experimental results show that the method achieves high F1-scores (up to 93.75%), providing more reliable and refined technical support for the detection and health assessment of complex underwater structures compared with conventional methods. Preservation of Critical Geometric Details: The model successfully mitigates under-segmentation of inconspicuous surfaces, ensuring that crucial geometric details for structural assembly and transition are accurately retained for engineering monitoring. 3D sonar is the primary technical means for the detection and health assessment of underwater structures. However, due to the harsh constraints of underwater imaging conditions, these point clouds experience severe noise interference and inconspicuous features. This complicates structural surface extraction and hinders the engineering monitoring process. To address this challenge, we propose an object-based energy-optimization segmentation method for precise extraction. First, the Voxel Cloud Connectivity Segmentation (VCCS) algorithm transforms massive discrete point clouds into semantically coherent supervoxel objects, significantly reducing computational complexity. Next, a global energy optimization framework is constructed, integrating a data term that characterizes structural surfaces and a smoothness term based on spatial neighborhood constraints. Through a graph-cut optimization procedure, this model enables the joint extraction of conspicuous and inconspicuous surfaces. Finally, a region-growing algorithm with multi-attribute constraints effectively filters typical sonar noise. Experimental results demonstrate the method's effectiveness, achieving an average F1-score of 89.19%. This approach maintains high segmentation accuracy and provides reliable technical support for underwater structure detection and assessment. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Global Energy Optimization Framework: A novel object-based energy optimization segmentation model is proposed, enabling the simultaneous and precise extraction of both large-area conspicuous planes and small-scale inconspicuous structural features from 3D sonar point clouds. Integrated Denoising and Complexity Reduction: By combining VCCS supervoxel clustering with a multi-attribute-constrained region-growing algorithm, the method significantly reduces computational costs and effectively filters typical sonar noise artifacts, such as scattering and multipath effects. What are the implications of the main findings? Enhanced Underwater Monitoring Accuracy: Experimental results show that the method achieves high F1-scores (up to 93.75%), providing more reliable and refined technical support for the detection and health assessment of complex underwater structures compared with conventional methods. Preservation of Critical Geometric Details: The model successfully mitigates under-segmentation of inconspicuous surfaces, ensuring that crucial geometric details for structural assembly and transition are accurately retained for engineering monitoring. 3D sonar is the primary technical means for the detection and health assessment of underwater structures. However, due to the harsh constraints of underwater imaging conditions, these point clouds experience severe noise interference and inconspicuous features. This complicates structural surface extraction and hinders the engineering monitoring process. To address this challenge, we propose an object-based energy-optimization segmentation method for precise extraction. First, the Voxel Cloud Connectivity Segmentation (VCCS) algorithm transforms massive discrete point clouds into semantically coherent supervoxel objects, significantly reducing computational complexity. Next, a global energy optimization framework is constructed, integrating a data term that characterizes structural surfaces and a smoothness term based on spatial neighborhood constraints. Through a graph-cut optimization procedure, this model enables the joint extraction of conspicuous and inconspicuous surfaces. Finally, a region-growing algorithm with multi-attribute constraints effectively filters typical sonar noise. Experimental results demonstrate the method's effectiveness, achieving an average F1-score of 89.19%. This approach maintains high segmentation accuracy and provides reliable technical support for underwater structure detection and assessment. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18162759 |
