From 2D Silhouettes to 3D Object Retrieval: Contributions and Benchmarking

Abstract

3D retrieval has recently emerged as an important boost for 2D search techniques. This is mainly due to its several complementary aspects, for instance, enriching views in 2D image datasets, overcoming occlusion and serving in many real-world applications such as photography, art, archeology, and geolocalization. In this paper, we introduce a complete “2D photography to 3D object” retrieval framework. Given a (collection of) picture(s) or sketch(es) of the same scene or object, the method allows us to retrieve the underlying similar objects in a database of 3D models. The contribution of our method includes (i) a generative approach for alignment able to find canonical views consistently through scenes/objects and (ii) the application of an efficient but effective matching method used for ranking. The results are reported through the Princeton Shape Benchmark and the Shrec benchmarking consortium evaluated/compared by a third party. In the two gallery sets, our framework achieves very encouraging performance and outperforms the other runs.