[CVPR 2019 Best Paper Award Winners] Shumian Xin & Ioannis Gkioulekas @ Carnegie Mellon University - Crossminds
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[CVPR 2019 Best Paper Award Winners] Shumian Xin & Ioannis Gkioulekas @ Carnegie Mellon University
Aug 08, 2020
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This episode features Shumian Xin and Prof. Ioannis Gkioulekas, co-authors of “A Theory of Fermat Paths for Non-Line-of-Sight Shape Reconstruction”, and winners of the CVPR 2019 Best Paper Award. They shared the details on the impressive results found from their recent research project, especially the importance of the collaborative experience within the development process, her enthusiasm about computational imagery, and the comparison with LiDAR sensors. View full interview transcripts at Robin.ly: http://bit.ly/2XBApiU Shumian is a 2nd-year PhD student and Ioannis is an assistant professor, both from the Robotics Institute of Carnegie Mellon University. They shared the details on the impressive results found from their recent research project, especially the importance of the collaborative experience within the development process, her enthusiasm about computational imagery, and the comparison with LiDAR sensors.
This episode features Shumian Xin and Prof. Ioannis Gkioulekas, co-authors of “A Theory of Fermat Paths for Non-Line-of-Sight Shape Reconstruction”, and winners of the CVPR 2019 Best Paper Award. They shared the details on the impressive results found from their recent research project, especially the importance of the collaborative experience within the development process, her enthusiasm about computational imagery, and the comparison with LiDAR sensors. View full interview transcripts at Robin.ly: http://bit.ly/2XBApiU Shumian is a 2nd-year PhD student and Ioannis is an assistant professor, both from the Robotics Institute of Carnegie Mellon University. They shared the details on the impressive results found from their recent research project, especially the importance of the collaborative experience within the development process, her enthusiasm about computational imagery, and the comparison with LiDAR sensors.
CVPR 2019
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