Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories

Recognizing object classes and their 3D viewpoints is an important problem in computer vision. Based on a partbased probabilistic representation [31], we propose a new 3D object class model that is capable of recognizing unseen views by pose estimation and synthesis. We achieve this by using a dense, multiview representation of the viewing sphere parameterized by a triangular mesh of viewpoints. Each triangle of viewpoints can be morphed to synthesize new viewpoints. By incorporating 3D geometrical constraints, our model establishes explicit correspondences among object parts across viewpoints. We propose an incremental learning algorithm to train the generative model. A cellphone video clip of an object is first used to initialize model learning. Then the model is updated by a set of unsorted training images without viewpoint labels. We demonstrate the robustness of our model on object detection, viewpoint classification and synthesis tasks. Our model performs superio...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Hao Su, Min Sun, Li Fei-Fei, Silvio Savarese
• Keywords: Computer Vision, Dense Multi-view, ICCV 2009, Viewpoint Classification