Energy Optimization-Based Segmentation and Extraction of 3D Sonar Point Clouds for Complex Underwater Structures

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.