Chi Fai Li

20180129865, May 10, 2018

Sep 20, 2017

15/709897

- Princeton NJ, US
Quoc-Huy Tran - Santa Clara CA, US
Xiang Yu - Mountain View CA, US
Manmohan Chandraker - Santa Clara CA, US
Chi Li - San Jose CA, US

G06K 9/00
G06T 15/10
G06N 3/02

An action recognition system and method are provided. The action recognition system includes an image capture device configured to capture an actual image depicting an object. The action recognition system includes a processor configured to render, based on a set of 3D CAD models, synthetic images with corresponding intermediate shape concept labels. The processor is configured to form a multi-layer CNN which jointly models multiple intermediate shape concepts, based on the rendered synthetic images. The processor is configured to perform an intra-class appearance variation-aware and occlusion-aware 3D object parsing on the actual image by applying the CNN thereto to generate an image pair including a 2D and 3D geometric structure of the object. The processor is configured to control a device to perform a response action in response to an identification of an action performed by the object, wherein the identification of the action is based on the image pair.

20180129910, May 10, 2018

Sep 20, 2017

15/709748

- Princeton NJ, US
Quoc-Huy Tran - Santa Clara CA, US
Xiang Yu - Mountain View CA, US
Manmohan Chandraker - Santa Clara CA, US
Chi Li - San Jose CA, US

G06K 9/62
G06T 15/40
G06K 9/00
G06N 3/02
G06F 17/50

A system and method are provided. The system includes an image capture device configured to capture an actual image depicting an object. The system also includes a processor. The processor is configured to render, based on a set of 3D Computer Aided Design (CAD) models, a set of synthetic images with corresponding intermediate shape concept labels. The processor is also configured to form a multi-layer Convolutional Neural Network (CNN) which jointly models multiple intermediate shape concepts, based on the rendered synthetic images. The processor is further configured to perform an intra-class appearance variation-aware and occlusion-aware 3D object parsing on the actual image by applying the CNN to the actual image to output an image pair including a 2D geometric structure and a 3D geometric structure of the object depicted in the actual image.

20180130229, May 10, 2018

Sep 20, 2017

15/709849

- Princeton NJ, US
Quoc-Huy Tran - Santa Clara CA, US
Xiang Yu - Mountain View CA, US
Manmohan Chandraker - Santa Clara CA, US
Chi Li - San Jose CA, US

G06T 7/73
G06N 3/08

A surveillance system and method are provided. The surveillance system includes an image capture device configured to capture an actual image of a target area depicting an object. The surveillance system further includes a processor. The processor is configured to render, based on a set of 3D Computer Aided Design (CAD) models, synthetic images with intermediate shape corresponding concept labels. The processor is further configured to form a multi-layer Convolutional Neural Network (CNN) which jointly models multiple intermediate shape concepts, based on the rendered synthetic images. The processor is also configured to perform an intra-class appearance variation-aware and occlusion-aware 3D object parsing on the actual image by applying the CNN to the actual image to generate an image pair including a 2D and 3D geometric structure of the object depicted in the actual image. The surveillance system further includes a display device configured to display the image pair.

20180130355, May 10, 2018

Sep 20, 2017

15/709814

- Princeton NJ, US
Quoc-Huy Tran - Santa Clara CA, US
Xiang Yu - Mountain View CA, US
Manmohan Chandraker - Santa Clara CA, US
Chi Li - San Jose CA, US

G08G 1/16
G06K 9/00
G05D 1/02

A system and method are provided for driving assistance. The system includes an image capture device configured to capture an actual image relative to an outward view from a motor vehicle and depicting an object. The system further includes a processor configured to render, based on a set of 3D CAD models, synthetic images with corresponding intermediate shape concept labels. The processor is further configured to form a multi-layer CNN which jointly models multiple intermediate shape concepts, based on the rendered synthetic images. The processor is also configured to perform an intra-class appearance variation-aware and occlusion-aware 3D object parsing on the actual image by applying the CNN to the actual image to output an image pair including a 2D and 3D geometric structure of the object. The processor is additionally configured to perform an action to mitigate a likelihood of harm involving the motor vehicle, based on the image pair.