News
Carnegie Mellon University and Argo AI Form Center for Autonomous Vehicle Research with $15-Million Multiyear Grant
Agreement Affirms Pittsburgh’s Status as the ‘Capital of Autonomy’ with CMU at the Center of the Growing Industry PITTSBURGH—Carnegie Mellon University and Argo AI today announced a five-year, $15 million sponsored research partnership under which the self-driving technology company will fund research into advanced perception[...]
Online Atlas of Aquatic Insects Aids Water-Quality Monitoring
New Tool Helps Even Novices Identify Insects Inhabiting Streams, Lakes and Rivers PITTSBURGH—A new online field guide to aquatic insects in the eastern United States, macroinvertebrates.org, promises to be an important tool for monitoring water quality, enabling even novices to correctly identify freshwater insects[...]
Best Paper, ICML 2019 Workshop on AI for Autonomous Driving
PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings N. Rhinehart, R. McAllister, K. M. Kitani, S. Levine Best Paper, ICML 2019 Workshop on AI for Autonomous Driving arXiv 2019 Workshop home page.
Congratulations to our four best paper finalists at the CVPR 2019 in Long Beach, CA.
“Neural RGB-> D Sensing: Depth and Uncertainty from a Video Camera,” by Chao Liu; Jinwei Gu; Kihwan Kim; Srinivasa G Narasimhan; Jan Kautz http://openaccess.thecvf.com/…/Liu_Neural_RGBrD_Sensing_Dep… “Shapes and Context; In-the-wild Image Synthesis & Manipulation, “Aayush Bansal; Yaser Sheikh; Deva Ramanan http://openaccess.thecvf.com/…/Bansal_Shapes_and_Context_In… “A Theory of Fermat Paths for[...]
CVPR 2019 Best Paper Award
Congratulations Shumian Xin, Ioannis Gkioulekas, Aswin Sankaranarayanan and Srinivasa Narasimhan and their U. Toronto colleagues for their CVPR Best Paper Award “A theory of Fermat Paths for Non-Line-of-Sight Shape Reconstruction” (out of ~5000 submitted papers). An amazing paper on non line of sight sensing. Read[...]
Researchers See Around Corners To Detect Object Shapes
Non-Line-of-Sight Technique Can Spot Washington’s Profile on a Quarter PITTSBURGH—Computer vision researchers have demonstrated they can use special light sources and sensors to see around corners or through gauzy filters, enabling them to reconstruct the shapes of unseen objects. The researchers from Carnegie Mellon University,[...]