3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with m...3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.展开更多
针对奶绵羊三维重构中背景分割对复杂场景适应性不足、配准算法对初始位置敏感等问题,该研究提出一种融合改进PointNet++与一致性点漂移(coherent point drift,CPD)算法与局部区域重叠的三维重构方法。通过引入点对特征、优化采样策略...针对奶绵羊三维重构中背景分割对复杂场景适应性不足、配准算法对初始位置敏感等问题,该研究提出一种融合改进PointNet++与一致性点漂移(coherent point drift,CPD)算法与局部区域重叠的三维重构方法。通过引入点对特征、优化采样策略及损失函数,增强了PointNet++在复杂场景下的分割能力;结合CPD算法与局部区域重叠策略,提升了点云配准的鲁棒性和效率。试验结果显示:该方法用于奶绵羊背景分割的准确率和平均交并比分别达到98.78%和97.25%,推理速度为53.4 ms;较原模型平均准确率和平均交并比分别提高了3.04和2.53个百分点,推理时间缩短了45.17%。该方法用于奶绵羊三维配准中,各向异性旋转误差、各向异性平移误差、各向同性旋转误差、各向同性平移误差以及倒角距离分别达到0.0256°、0.0229 m、3.0887°、0.0463 m和0.00789 m,较原始CPD方法均降低。通过与人工体尺测量数据对比,重构模型所提取的体长、体高、十字部高、胸深、胸围等参数的平均绝对百分比误差分别为3.34%、3.07%、3.32%、3.63%和2.81%。该研究方法兼具较高精度与实时性,能够满足一次性重构的需求,可为奶绵羊三维配准与智能化体尺测定提供参考。展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52304139,52325403)the CCTEG Coal Mining Research Institute funding(Grant No.KCYJY-2024-MS-10).
文摘3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.
