Feature analysis plays a crucial role in various applications in both computer vision and computer graphics. The semantic gap between 2D images and 3D graphical models is the major obstacle to improve the universality of existing valuable technologies. To bridge the gap, we propose an effective and robust representation of 3D models, named multi-scale Graphical Image (GI), which is constructed by introducing the statistics mapping from 3D models to 2D images with both local and global information. Therefore, the excellent innovations and techniques in 2D visual retrieval can be adapted to 3D geometric retrieval. In the multi-scale GI space, the joint Bayesian formulation is exploited to analyze the structure of the space and learn a new metric. It benefits several attractive properties, including high discriminative, isometric invariant and robust to noise and topological changes, etc. In order to prove the validity, we apply the proposed method to 3D shape retrieval, and test our method on two well-known benchmark datasets. The results show that our method substantially outperforms the state-of-the-art non-rigid 3D shape retrieval methods.

特征分析在计算机视觉和计算机图形学的各种应用中都起着至关重要的作用。2D图像和3D图形模型之间的语义鸿沟是提高现有有价值技术的通用性的主要障碍。为了弥合差距，我们提出了一种有效且鲁棒的3D模型表示方法，称为多尺度图形图像（GI），它是通过引入从3D模型到具有本地和全局信息的2D图像的统计信息映射而构建的。因此，2D视觉检索中的出色创新和技术可以适应3D几何检索。在多尺度GI空间中，联合贝叶斯公式被用来分析空间结构并学习新的度量。它具有多种吸引人的特性，包括高判别力，为了证明有效性，我们将提出的方法应用于3D形状检索，并在两个著名的基准数据集上对该方法进行了测试。结果表明，我们的方法大大优于最新的非刚性3D形状检索方法。