Zheng Xiaoxiong, Weng Zhenwei
School of Sculpture and Public Art, Guangzhou Academy of Fine Arts, Guangzhou, Guangdong, China.
Creation and Arts College, Universiti Teknologi MARA (Uitm), Shah Anam, Selangor, Malaysia.
PeerJ Comput Sci. 2025 Jan 3;11:e2628. doi: 10.7717/peerj-cs.2628. eCollection 2025.
As the aesthetic appreciation for art continues to grow, there is an increased demand for precision and detailed control in sculptural works. The advent of 3D laser scanning technology introduces transformative new tools and methodologies for refining correction systems in sculpture design. This article proposes a feature point matching algorithm based on fragment measurement and the iterative closest point (ICP) methodology, leveraging 3D laser scanning technology, namely Fragment Measurement Iterative Closest Point Feature Point Matching (FM-ICP-FPM). The FM-ICP-FPM approach uses the overlapping area of the two sculpture perspectives as a reference for attaching feature points. It employs the 3D measurement system to capture physical point cloud data from the two surfaces to enable the initial alignment of feature points. Feature vectors are generated by segmenting the region around the feature points and computing the intra-block gradient histogram. Subsequently, distance threshold conditions are set based on the constructed feature vectors and the preliminary feature point matches established during the coarse alignment to achieve precise feature point matching. Experimental results demonstrate the exceptional performance of the FM-ICP-FPM algorithm, achieving a sampling interval of 200. The correct matching rate reaches an impressive 100%, while the mean translation error (MTE) is a mere 154 mm, and the mean rotation angle error (MRAE) is 0.065 degrees. The indicator represents the degree of deviation in translation and rotation of the registered model, respectively. These low error values demonstrate that the FM-ICP-FPM algorithm excels in registration accuracy and can generate highly consistent three-dimensional models.
随着对艺术美学鉴赏的不断提升，雕塑作品对精度和细节控制的需求也日益增加。3D激光扫描技术的出现为雕塑设计中的精确校正系统引入了变革性的新工具和方法。本文提出了一种基于片段测量和迭代最近点（ICP）方法的特征点匹配算法，利用3D激光扫描技术，即片段测量迭代最近点特征点匹配（FM-ICP-FPM）。FM-ICP-FPM方法以两个雕塑视角的重叠区域为参考来附着特征点。它使用3D测量系统从两个表面捕获物理点云数据，以实现特征点的初始对齐。通过对特征点周围区域进行分割并计算块内梯度直方图来生成特征向量。随后，根据构建的特征向量和在粗对齐过程中建立的初步特征点匹配设置距离阈值条件，以实现精确的特征点匹配。实验结果表明FM-ICP-FPM算法具有卓越的性能，采样间隔为200。正确匹配率达到了令人印象深刻的100%，而平均平移误差（MTE）仅为154毫米，平均旋转角度误差（MRAE）为0.065度。该指标分别表示配准模型在平移和旋转方面的偏差程度。这些低误差值表明FM-ICP-FPM算法在配准精度方面表现出色，能够生成高度一致的三维模型。
