Snap Inc.

Xutao Lv

Jia Li

Jonathan M Rodriguez, Ii

Linjie Luo

Rahul Bhupendra Sheth

Nathan Jurgenson

Xiaoyu Wang

Jianchao Yang

Ning Zhang

Zhou Ren

Xuehan Xiong

Jonathan M Ii Rodriguez

Systems and methods for image based location estimation are described. In one example embodiment, a first positioning system is used to generate a first position estimate. Point cloud data describing an environment is then accessed. A two-dimensional surface of an image of an environment is captured, and a portion of the image is matched to a portion of key points in the point cloud data. An augmented reality object is then aligned within one or more images of the environment based on the match of the point cloud with the image. In some embodiments, building façade data may additionally be used to determine a device location and place the augmented reality object within an image.

Image and point cloud based tracking and in augmented reality systems

Systems and methods for image based location estimation are described. In one example embodiment, a first positioning system is used to generate a first position estimate. A set of structure fa&#xe7;ade data describing one or more structure fa&#xe7;ades associated with the first position estimate is then accessed. A first image of an environment is captured, and a portion of the image is matched to part of the structure fa&#xe7;ade data. A second position is then estimated based on a comparison of the structure fa&#xe7;ade data with the portion of the image matched to the structure fa&#xe7;ade data.

Image based tracking in augmented reality systems

Systems and methods for image based location estimation are described. In one example embodiment, a first positioning system is used to generate a first position estimate. Point cloud data describing an environment is then accessed. A two-dimensional surface of an image of an environment is captured, and a portion of the image is matched to a portion of key points in the point cloud data. An augmented reality object is then aligned within one or more images of the environment based on the match of the point cloud with the image. In some embodiments, building fa&#xe7;ade data may additionally be used to determine a device location and place the augmented reality object within an image.

An image captioning system and method is provided for generating a caption for an image. The image captioning system utilizes a policy network and a value network to generate the caption. The policy network serves as a local guidance and the value network serves as a global and lookahead guidance.

Deep reinforcement learning-based captioning with embedding reward

Systems, devices, media, and methods are presented for modeling facial representations using image segmentation with a client device. The systems and methods receive an image depicting a face, detect at least a portion of the face within the image, and identify a set of facial features within the portion of the face. The systems and methods generate a descriptor function representing the set of facial features, fit object functions of the descriptor function, identify an identification probability for each facial feature, and assign an identification to each facial feature.

Neural networks for facial modeling

Systems and methods for image based location estimation are described. In one example embodiment, a first positioning system is used to generate a first position estimate. A 3D point cloud data describing an environment is then accessed. A first image of an environment is captured, and a portion of the image is matched to a portion of key points in the 3D point cloud data. An augmented reality object is then aligned within one or more images of the environment based on the match of the 3D point cloud with the image. In some embodiments, building fa&#xe7;ade data may additionally be used to determine a device location and place the augmented reality object within an image.

Growing community of inventors

Marina Del Rey, CA, United States of America

Xutao Lv