公开(公告)号：US10255529B2

公开(公告)日：2019-04-09

申请号：US15457990

申请日：2017-03-13

Applicant: Magic Leap, Inc.

Inventor： Andrew Rabinovich ,  Vijay Badrinarayanan ,  Daniel DeTone ,  Srivignesh Rajendran ,  Douglas Bertram Lee ,  Tomasz Malisiewicz

IPC: G06K9/00 ,  G06K9/66 ,  G06K9/62 ,  G06K9/46

Abstract: The present disclosure provides an improved approach to implement structure learning of neural networks by exploiting correlations in the data/problem the networks aim to solve. A greedy approach is described that finds bottlenecks of information gain from the bottom convolutional layers all the way to the fully connected layers. Rather than simply making the architecture deeper, additional computation and capacitance is only added where it is required.

公开(公告)号：US20190087973A1

公开(公告)日：2019-03-21

申请号：US16134600

申请日：2018-09-18

Applicant: Magic Leap, Inc.

Inventor： Adrian Kaehler ,  Douglas Lee ,  Vijay Badrinarayanan

IPC: G06T7/70 ,  G06T7/20 ,  G06F3/01

Abstract: Disclosed herein is a wearable display system for capturing retraining eye images of an eye of a user for retraining a neural network for eye tracking. The system captures retraining eye images using an image capture device when user interface (UI) events occur with respect to UI devices displayed at display locations of a display. The system can generate a retraining set comprising the retraining eye images and eye poses of the eye of the user in the retraining eye images (e.g., related to the display locations of the UI devices) and obtain a retrained neural network that is retrained using the retraining set.

公开(公告)号：US20180268220A1

公开(公告)日：2018-09-20

申请号：US15923511

申请日：2018-03-16

Applicant: Magic Leap, Inc.

Inventor： Chen-Yu Lee ,  Vijay Badrinarayanan ,  Tomasz Malisiewicz ,  Andrew Rabinovich

IPC: G06K9/00 ,  G06N3/04 ,  G06K9/34 ,  G06K9/46

Abstract: Systems and methods for estimating a layout of a room are disclosed. The room layout can comprise the location of a floor, one or more walls, and a ceiling. In one aspect, a neural network can analyze an image of a portion of a room to determine the room layout. The neural network can comprise a convolutional neural network having an encoder sub-network, a decoder sub-network, and a side sub-network. The neural network can determine a three-dimensional room layout using two-dimensional ordered keypoints associated with a room type. The room layout can be used in applications such as augmented or mixed reality, robotics, autonomous indoor navigation, etc.