Toward Dense 3D Reconstruction in the Wild

Toward Dense 3D Reconstruction in the Wild (via Zoom)

Abstract: Reconstructing depth and motion of every pixel for arbitrary scenes is a core problem in 3D vision with many downstream applications. In this talk, I will describe some of our recent efforts toward this goal, including various strategies to obtain effective training data for single-image 3D reconstruction, and new neural architectures that advance the state of the art of multiview 3D reconstruction.

Bio: Jia Deng is an Assistant Professor of Computer Science at Princeton University. His research focuses on computer vision and machine learning. He is a recipient of the Sloan Research Fellowship, the NSF CAREER award, the ONR Young Investigator award, an ICCV Marr Prize, a CVPR test-of-time award, a 3DV Best Student Paper Award, and two ECCV Best Paper Awards.