3D Articulated Model Retrieval Using Depth Image Input

Junyang Lin, May-Fang She, Ming-Han Tsai, I-Chen Lin, Yo-Chung Lau, Hsu-Hang Liu

Abstract: In this paper, a novel framework to retrieve 3D articulated models from a database based on one or few depth images is presented. Existing state-of-the-arts retrieval approaches usually constrain the view points of query images or assume that the target models are rigid-body. When they are applied to retrieving articulated models, the retrieved results are substantially influenced by the model postures. In our work, we extracts the limbs and torso regions from projections and analyzes the features of local regions. The use of both global and local features can alleviate the disturbance of model postures in model retrieval. Experiments show that the proposed method can efficiently retrieve relevant models within a second, and provides higher retrieval accuracy than those of compared methods for not only rigid body 3D models but also models with articulated limbs.