Sri International

Ryan Villamil

Rakesh Kumar

Supun Samarasekera

Harpreet Singh Sawhney

Bogdan C Matei

Raia Hadsell

Girish Acharya

Michael John Wolverton

Zhiwei Zhu

Han-Pang Chiu

Necip Fazil Ayan

James R Carpenter

Mikhail Sizintsev

Niluthpol C Mithun

Zachary Seymour

Vlad Branzoi

Glenn Murray

Philip Miller

Alex Krasner

Abhinav Rajvanshi

Varun Murali

Louise Yarnall

Gregory Drew Kessler

Junjiao Tian

Supun Samarasekara

Kevin Kaighn

A method, machine readable medium and system for RGBD semantic segmentation of video data includes determining semantic segmentation data and depth segmentation data for less than all classes for images of each frame of a first video, determining semantic segmentation data and depth segmentation data for images of each key frame of a second video including a synchronous combination of respective frames of the RGB video and the depth-aware video in parallel to the determination of the semantic segmentation data and the depth segmentation data for each frame of the first video, temporally and geometrically aligning respective frames of the first video and the second video, and predicting semantic segmentation data and depth segmentation data for images of a subsequent frame of the first video based on the determination of the semantic segmentation data and depth segmentation data for images of a key frame of the second video.

RGBD video semantic segmentation with temporal and geometric consistency

Techniques for augmenting a reality captured by an image capture device are disclosed. In one example, a system includes an image capture device that generates a two-dimensional frame at a local pose. The system further includes a computation engine executing on one or more processors that queries, based on an estimated pose prior, a reference database of three-dimensional mapping information to obtain an estimated view of the three-dimensional mapping information at the estimated pose prior. The computation engine processes the estimated view at the estimated pose prior to generate semantically segmented sub-views of the estimated view. The computation engine correlates, based on at least one of the semantically segmented sub-views of the estimated view, the estimated view to the two-dimensional frame. Based on the correlation, the computation engine generates and outputs data for augmenting a reality represented in at least one frame captured by the image capture device.

Augmenting reality using semantic segmentation

A computing system for virtual personal assistance includes technologies to, among other things, correlate an external representation of an object with a real world view of the object, display virtual elements on the external representation of the object and/or display virtual elements on the real world view of the object, to provide virtual personal assistance in a multi-step activity or another activity that involves the observation or handling of an object and a reference document.

Augmented reality virtual personal assistant for external representation

A method and apparatus for training and guiding users comprising generating a scene understanding based on video and audio input of a scene of a user performing a task in the scene, correlating the scene understanding with a knowledge base to produce a task understanding, comprising one or more goals, of a current activity of the user, reasoning, based on the task understanding and a user's current state, a next step for advancing the user towards completing one of the one or more goals of the task understanding and overlaying the scene with an augmented reality view comprising one or more visual and audio representation of the next step to the user.

Method and apparatus for mentoring via an augmented reality assistant

A multi-sensor, multi-modal data collection, analysis, recognition, and visualization platform can be embodied in a navigation capable vehicle. The platform provides an automated tool that can integrate multi-modal sensor data including two-dimensional image data, three-dimensional image data, and motion, location, or orientation data, and create a visual representation of the integrated sensor data, in a live operational environment. An illustrative platform architecture incorporates modular domain-specific business analytics 'plug ins' to provide real-time annotation of the visual representation with domain-specific markups.

Real-time system for multi-modal 3D geospatial mapping, object recognition, scene annotation and analytics

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Plainsboro, NJ, United States of America

Ryan Villamil