From 2D Silhouettes to 3D Object Retrieval: Contributions and Benchmarking
Résumé
3D object recognition and retrieval recently gained a big interest because of the limitation of the “2D-to-2D” approaches. The latter suffer from several drawbacks such as the lack of information (due for instance to occlusion), pose sensitivity, illumination changes, etc. Our main motivation is to gather both discrimination and easy interaction by allowing simple (but multiple) 2D specifications of queries and their retrieval into 3D gallery sets. We introduce a novel “2D sketch-to-3D model” retrieval framework with the following contributions: (i) first a novel generative approach for aligning and normalizing the pose of 3D gallery objects and extracting their 2D canonical views is introduced. (ii) Afterwards, robust and compact contour signatures are extracted using the set of 2D canonical views. We also introduce a pruning approach to speedup the whole search process in a coarse-to-fine way. (iii) Finally, object ranking is performed using our variant of elastic dynamic programming which considers only a subset of possible matches thereby providing a considerable gain in performance for the same amount of errors. Our experiments are reported/compared through the Princeton Shape Benchmark; clearly showing the good performance of our framework w.r.t. the other approaches. An iPhone demo of this method is available and allows us to achieve “2D sketch to 3D object” querying and interaction.