Object recognition from images is a longstanding and challenging problem in computer vision. The main challenge is that the appearance of objects in images is affected by a number of factors, such as illumination, scale, camera viewpoint, intra-class variability, occlusion, truncation, and so on. How to handle all these factors in object recognition is still an open problem. In this talk, I present my efforts in building 3D object representations for object recognition. Compared to 2D appearance based object representations, 3D object representations can capture the 3D nature of objects and better handle viewpoint variation, occlusion and truncation in object recognition. I will also talk about our work on building benchmark datasets for 3D object recognition.
Yu Xiang is a Postdoctoral Researcher in the Computer Science Department at Stanford University. His research focuses on understanding objects and scenes from images and videos, with emphasis on recognizing both semantic and 3D geometric properties of objects and scenes. His current work attempts to develop 3D object representation and recognition methods that can be useful for real world applications. Yu Xiang received his Ph.D. in computer vision from the University of Michigan in 2015, M.S. degree in computer science from Fudan University in 2010, and B.S. degree in computer science from Fudan University in 2007.