2D-Based 3D Volume Retrieval Using Singular Value Decomposition of Detected Regions

In this paper, a novel 3D retrieval model to retrieve medical volumes using 2D images as input is proposed. The main idea consists of applying a multi–scale detection of saliency of image regions. Then, the 3D volumes with the regions for each of the scales are associated with a set of projections onto the three canonical planes. The 3D shape is indirectly represented by a 2D–shape descriptor so that the 3D–shape matching is transformed into measuring similarity between 2D–shapes. The shape descriptor is defined by the set of the k largest singular values of the 2D images and Euclidean distance between the vector descriptors is used as a similarity measure. The preliminary results obtained on a simple database show promising performance with a mean average precision (MAP) of 0.82 and could allow using the approach as part of a retrieval system in clinical routine

Content-based 3D object retrieval using 2D views

International audience2D techniques have recently emerged as an important boost for 3D objects content-based retrieval in many real world applications such as photography, art, archeology and geolocalization thanks to its several complementary aspects. We introduce in this paper a new framework for 3D objects content-based retrieval based on a 2D photography approach. A new alignment process that is able to find canonical views consistently through scenes/objects and a new coarse-to-fine description and matching method used for ranking are our contributions. The results are presented through an international benchmarking and showing clearly the good performance of our framework with respect to the other participants