Local features and manifold ranking coupled method for sketch-based 3D model retrieval
Local features and manifold ranking coupled method for sketch-based 3D model retrieval
复制标题
基于草图的 3D 模型检索的局部特征和流形排序耦合方法
DOI：
10.1007/s11704-017-6595-6
复制
发表时间：
2018-10-01
影响因子：
4.2
通讯作者：
Guo, Ruiliang
中科院分区：
文献类型：
--
作者：
Tan, Xiaohui;Fan, Yachun;Guo, Ruiliang
3D模型检索可以使许多下游虚拟现实应用受益。在本文中，我们提出了一个新的基于草图的三维模型检索框架耦合的局部特征和流形排名。在技术方面，我们利用基于空间金字塔的局部结构来促进特征描述符的有效构建。同时，我们提出了一种改进的流形排序方法，其中任意模型对之间的所有类别将被考虑在内。针对三维模型中光滑且细节保持良好的线条特征对基于草图的三维模型检索的重要性，采用高斯差分法（DoG）提取三维模型投影深度图像上的线条特征，并采用Bezier曲线对提取的线条特征进行优化。在此基础上，我们开发了一个三维模型检索引擎来验证我们的方法。我们在各种公共基准上进行了广泛的实验，并与一些最先进的3D检索方法进行了全面的比较。基于广泛使用的指标的所有评价结果证明了我们的方法在准确性，可靠性，鲁棒性和通用性的优越性。
3D model retrieval can benefit many downstream virtual reality applications. In this paper, we propose a new sketch-based 3D model retrieval framework by coupling local features and manifold ranking. At technical fronts, we exploit spatial pyramids based local structures to facilitate the efficient construction of feature descriptors. Meanwhile, we propose an improved manifold ranking method, wherein all the categories between arbitrary model pairs will be taken into account. Since the smooth and detail-preserving line drawings of 3D model are important for sketch-based 3D model retrieval, the Difference of Gaussians (DoG) method is employed to extract the line drawings over the projected depth images of 3D model, and Bezier Curve is then adopted to further optimize the extracted line drawing. On that basis, we develop a 3D model retrieval engine to verify our method. We have conducted extensive experiments over various public benchmarks, and have made comprehensive comparisons with some state-of-the-art 3D retrieval methods. All the evaluation results based on the widely-used indicators prove the superiority of our method in accuracy, reliability, robustness, and versatility.