Event Location: NSH 1507
Bio: Abhinav Shrivastava is a PhD student in the Robotics Institute at Carnegie Mellon University, where he is supervised by Abhinav Gupta and Alexei A. Efros. He received his Master’s degree from the same institute under the supervision of A. A. Efros and Martial Hebert. His research interests span computer vision esp. data-driven methods for image/scene understanding, object recognition and representation learning, computer graphics, and large-scale applied machine learning.

Abstract: This work proposes a novel part-based representation for modeling object categories. Our representation combines the effectiveness of deformable part-based models with the richness of geometric representation by defining parts based on consistent underlying 3D geometry. Our key hypothesis is that while the appearance and the arrangement of parts might vary across the instances of object categories, the constituent parts will still have consistent underlying 3D geometry. We propose to learn this geometry-driven deformable part-based model (gDPM) from a set of labeled RGBD images. We also demonstrate how the geometric representation of gDPM can help us leverage depth data during training and constrain the latent model learning problem. But most importantly, a joint geometric and appearance based representation not only allows us to achieve state-of-the-art results on object detection but also allows us to tackle the challenge of understanding 3D objects from 2D images.