Title:
Perception at Magic Leap
Perception at Magic Leap
dc.contributor.author | Swaminathan, Ashwin | |
dc.contributor.corporatename | Georgia Institute of Technology. Machine Learning | en_US |
dc.contributor.corporatename | Magic Leap | en_US |
dc.date.accessioned | 2019-05-08T13:59:33Z | |
dc.date.available | 2019-05-08T13:59:33Z | |
dc.date.issued | 2019-04-19 | |
dc.description | Presented on April 19, 2019 at 2:00 p.m. in the Marcus Nanotechnology Building, Room 1116. | en_US |
dc.description | Ashwin Swaminathan is part of computer vision team at Magic Leap where he leads a team researching on simultaneous localization and mapping, object recognition, visual inertial odometry, and scene semantics. Prior to joining Magic Leap, he was with Qualcomm Research in San Diego from 2008 to 2015. At Qualcomm Research, he was involved in various computer vision and machine learning projects for applications in augmented reality and context-aware computing on mobile phones. In addition, he was involved in Qualcomm’s efforts in robotics and drones. | en_US |
dc.description | Runtime: 51:54 minutes | en_US |
dc.description.abstract | This talk presents the importance of Computer Vision and Deep learning techniques in making Magic Leap an effective spatial computing platform. The four fundamental modalities are introduced: head pose tracking, world reconstruction, eye tracking, and hand tracking; emphasizing on the two main general themes: Understanding the world (spatial localization, environment mapping) and Understanding user’s intent (eye, gaze, and hands). The talk will provide a deep dive into the main modalities along with key challenges and open problems. We will go over some technical challenges that our team of researchers and engineers are tacking to make computer vision work for see-through wearable Mixed Reality device. We will start with an overview of the past 5 years of development in inside-out pose estimation (Visual Inertial Odometry), scene reconstruction, eye tracking, and sensor calibration and then dive into the long list of technical challenges that are left to solve. | en_US |
dc.format.extent | 51:54 minutes | |
dc.identifier.uri | http://hdl.handle.net/1853/61042 | |
dc.language.iso | en_US | en_US |
dc.relation.ispartofseries | Machine Learning @ Georgia Tech (ML@GT) Seminar Series | |
dc.subject | Augmented reality | en_US |
dc.subject | Computer vision | en_US |
dc.subject | Deep learning | en_US |
dc.title | Perception at Magic Leap | en_US |
dc.type | Moving Image | |
dc.type.genre | Lecture | |
dspace.entity.type | Publication | |
local.contributor.corporatename | Machine Learning Center | |
local.contributor.corporatename | College of Computing | |
local.relation.ispartofseries | ML@GT Seminar Series | |
relation.isOrgUnitOfPublication | 46450b94-7ae8-4849-a910-5ae38611c691 | |
relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
relation.isSeriesOfPublication | 9fb2e77c-08ff-46d7-b903-747cf7406244 |
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