CAJ | 학술논문

面向三维模型的统一结构描述与智能检索的技术需求, 提出一种Laplacian多特征映射的三维模型形状分析方法. 首先提取三维模型的表面形状与体积特征, 建立融合测地线距离、角距离和空间体积的多特征相似度矩阵; 其次根据 Laplacian 特征映射算法实现三维模型由空域到谱域的转换以及多谱特征分析; 最后通过对 Laplacian 矩阵特征值之间的本征间隙自适应确定聚类数目, 并结合 K-means 聚类方法实现模型的自动结构识别与分割. 实验结果表明, 在同一类模型的结构特征提取与统一分割应用中, 该方法是高效、鲁棒的, 对于实现模型的高层次语义描述、模型配准以及模型检索具有重要的意义.
면향삼유모형적통일결구묘술여지능검색적기술수구, 제출일충Laplacian다특정영사적삼유모형형상분석방법. 수선제취삼유모형적표면형상여체적특정, 건립융합측지선거리、각거리화공간체적적다특정상사도구진; 기차근거 Laplacian 특정영사산법실현삼유모형유공역도보역적전환이급다보특정분석; 최후통과대 Laplacian 구진특정치지간적본정간극자괄응학정취류수목, 병결합 K-means 취류방법실현모형적자동결구식별여분할. 실험결과표명, 재동일류모형적결구특정제취여통일분할응용중, 해방법시고효、로봉적, 대우실현모형적고층차어의묘술、모형배준이급모형검색구유중요적의의.
Aiming at the demands of consistent descriptor and intelligent retrieval technology for 3D shapes, we pro-pose a 3D shape analysis based on Laplacian multi-eigenmap. Firstly, we extract the surface and volumetric features of 3D models and construct a multi-feature affinity matrix based on the measurement of geometric distance, angular dis-tance and volumetric distance. Secondly, our method converts 3D spatial domain to spectral domain by using Laplacian multi-eigenmap which effectively reveals the intrinsic invariance and consistent structure among shapes. Finally, we analyze the eigengap to adaptively determine the clustering number and implement automatic structural recognition and segmentation by combining theK-means clustering method. A series of experimental results have shown its robustness and efficiency in shape matching and shape segmentation. Our work has important significance for the high-level se-mantic description, shape registration and shape retrieval.