View-based 3D model retrieval using compressive sensing based classification

The number of available 3D models in various areas increases steadily. Efficient methods to search for 3D models by content, rather than textual annotations, are crucial. For this purpose, we propose content based 3D model retrieval using a compressive sensing technique which is very efficient in classification by using only few input information. Our approach to search and automatically return a set of 3D mesh models from a large database consists of three major steps: (1) suggestive contours extraction from different viewpoints to extract features of the query 3D model; (2) descriptor computation by analyzing the Histogram of Oriented Gradients of the suggestive contours in the space of diffusion tensor fields; and (3) compressive sensing based machine learning to retrieve the models and the most probable view-point. Experimental results show that our proposed 3D model re trieval system is very effective to retrieve the 3D models, even though there are variations of shape and pose of the models.