Patents by Inventor Shenlong Wang

Shenlong Wang has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20190147253
    Abstract: Systems and methods for facilitating communication with autonomous vehicles are provided. In one example embodiment, a computing system can obtain rasterized LIDAR data associated with a surrounding environment of an autonomous vehicle. The rasterized LIDAR data can include LIDAR image data that is rasterized from a LIDAR point cloud. The computing system can access data indicative of a machine-learned lane boundary detection model. The computing system can input the rasterized LIDAR data associated with the surrounding environment of the autonomous vehicle into the machine-learned lane boundary detection model. The computing system can obtain an output from the machine-learned lane boundary detection model. The output can be indicative of one or more lane boundaries within the surrounding environment of the autonomous vehicle.
    Type: Application
    Filed: September 5, 2018
    Publication date: May 16, 2019
    Inventors: Min Bai, Gellert Sandor Mattyus, Namdar Homayounfar, Shenlong Wang, Shrindihi Kowshika Lakshmikanth, Raquel Urtasun, Wei-Chiu Ma
  • Publication number: 20190147254
    Abstract: Systems and methods for facilitating communication with autonomous vehicles are provided. In one example embodiment, a computing system can obtain a first type of sensor data (e.g., camera image data) associated with a surrounding environment of an autonomous vehicle and/or a second type of sensor data (e.g., LIDAR data) associated with the surrounding environment of the autonomous vehicle. The computing system can generate overhead image data indicative of at least a portion of the surrounding environment of the autonomous vehicle based at least in part on the first and/or second types of sensor data. The computing system can determine one or more lane boundaries within the surrounding environment of the autonomous vehicle based at least in part on the overhead image data indicative of at least the portion of the surrounding environment of the autonomous vehicle and a machine-learned lane boundary detection model.
    Type: Application
    Filed: September 5, 2018
    Publication date: May 16, 2019
    Inventors: Min Bai, Gellert Sandor Mattyus, Namdar Homayounfar, Shenlong Wang, Shrindihi Kowshika Lakshmikanth, Raquel Urtasun
  • Publication number: 20190145784
    Abstract: Systems and methods for autonomous vehicle localization are provided. In one example embodiment, a computer-implemented method includes obtaining, by a computing system that includes one or more computing devices onboard an autonomous vehicle, sensor data indicative of one or more geographic cues within the surrounding environment of the autonomous vehicle. The method includes obtaining, by the computing system, sparse geographic data associated with the surrounding environment of the autonomous vehicle. The sparse geographic data is indicative of the one or more geographic cues. The method includes determining, by the computing system, a location of the autonomous vehicle within the surrounding environment based at least in part on the sensor data indicative of the one or more geographic cues and the sparse geographic data. The method includes outputting, by the computing system, data indicative of the location of the autonomous vehicle within the surrounding environment.
    Type: Application
    Filed: September 6, 2018
    Publication date: May 16, 2019
    Inventors: Wei-Chiu Ma, Shenlong Wang, Namdar Homayounfar, Shrinidhi Kowshika Lakshmikanth, Raquel Urtasun
  • Publication number: 20190147335
    Abstract: Systems and methods are provided for machine-learned models including convolutional neural networks that generate predictions using continuous convolution techniques. For example, the systems and methods of the present disclosure can be included in or otherwise leveraged by an autonomous vehicle. In one example, a computing system can perform, with a machine-learned convolutional neural network, one or more convolutions over input data using a continuous filter relative to a support domain associated with the input data, and receive a prediction from the machine-learned convolutional neural network. A machine-learned convolutional neural network in some examples includes at least one continuous convolution layer configured to perform convolutions over input data with a parametric continuous kernel.
    Type: Application
    Filed: October 30, 2018
    Publication date: May 16, 2019
    Inventors: Shenlong Wang, Wei-Chiu Ma, Shun Da Suo, Raquel Urtasun, Ming Liang
  • Patent number: 9886652
    Abstract: Correspondences in content items may be determined using a trained decision tree to detect distinctive matches between portions of content items. The techniques described include determining a first group of patches associated with a first content item and processing a first patch based at least partly on causing the first patch to move through a decision tree, and determining a second group of patches associated with a second content item and processing a second patch based at least partly on causing the second patch to move through the decision tree. The techniques described include determining that the first patch and the second patch are associated with a same leaf node of the decision tree and determining that the first patch and the second patch are corresponding patches based at least partly on determining that the first patch and the second patch are associated with the same leaf node.
    Type: Grant
    Filed: March 15, 2016
    Date of Patent: February 6, 2018
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Sean Ryan Francesco Fanello, Shahram Izadi, Pushmeet Kohli, Christoph Rhemann, Shenlong Wang
  • Publication number: 20170270390
    Abstract: Correspondences in content items may be determined using a trained decision tree to detect distinctive matches between portions of content items. The techniques described include determining a first group of patches associated with a first content item and processing a first patch based at least partly on causing the first patch to move through a decision tree, and determining a second group of patches associated with a second content item and processing a second patch based at least partly on causing the second patch to move through the decision tree. The techniques described include determining that the first patch and the second patch are associated with a same leaf node of the decision tree and determining that the first patch and the second patch are corresponding patches based at least partly on determining that the first patch and the second patch are associated with the same leaf node.
    Type: Application
    Filed: March 15, 2016
    Publication date: September 21, 2017
    Inventors: Sean Ryan Francesco Fanello, Shahram Izadi, Pushmeet Kohli, Christoph Rhemann, Shenlong Wang