LAUSR

Recently, with the rapid development of digital technologies and its wide application, 3D model retrieval is becoming more and more important in graphic communities. In this task, how to effectively represent the 3D model and how to robustly measure similarity between pair-wise models are two crucial problems. In previous work, most papers dedicated to researching how to effectively using the visualize features to represent 3D model and using the visual information to measure the similarity. However, visual feature can not represent 3D model well because of the model variations in poses and illumination. To address this task, we propose an novel framework, which utilizes the visual and contextual information to construct the rank graphs and fuses these two graphs to enhance the similarity measure. When fusing visual and contextual information, we define four strategies to measure the similarity among models according to the relation between the query model and the gallery models. The extensive experimental results demonstrate the superiority of our proposed method compare against the state of the arts.