Patents by Inventor Xueting Li

Xueting Li 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).

  • Patent number: 11456950
    Abstract: The present invention discloses a data forwarding unit based on a Handle identifier, comprising a dynamic configuration module, a Handle identifier data identification module and a matching-forwarding module. The system of the present invention is applied to network devices such as switches and routers, and supports dynamic configuration of data packet analysis, matching and forwarding rules through data interaction with network systems such as SDN managers, so that the network devices can identify data packets based on the Handle identifier and perform the specified operation on the designated data packets with the Handle identifier according to the rules of dynamic configuration.
    Type: Grant
    Filed: December 19, 2019
    Date of Patent: September 27, 2022
    Assignee: SHENYANG INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES
    Inventors: Haibin Yu, Peng Zeng, Dong Li, Zhibo Li, Jindi Liu, Xueting Yu, Ming Yang
  • Publication number: 20220270318
    Abstract: A three-dimensional (3D) object reconstruction neural network system learns to predict a 3D shape representation of an object from a video that includes the object. The 3D reconstruction technique may be used for content creation, such as generation of 3D characters for games, movies, and 3D printing. When 3D characters are generated from video, the content may also include motion of the character, as predicted based on the video. The 3D object construction technique exploits temporal consistency to reconstruct a dynamic 3D representation of the object from an unlabeled video. Specifically, an object in a video has a consistent shape and consistent texture across multiple frames. Texture, base shape, and part correspondence invariance constraints may be applied to fine-tune the neural network system. The reconstruction technique generalizes well—particularly for non-rigid objects.
    Type: Application
    Filed: May 2, 2022
    Publication date: August 25, 2022
    Inventors: Xueting Li, Sifei Liu, Kihwan Kim, Shalini De Mello, Jan Kautz
  • Patent number: 11370732
    Abstract: The present disclosure provides a method of separating ?-olefin by a simulated moving bed. The method comprises using a coal-based Fischer-Tropsch synthetic oil as a raw material to obtain a target olefin having a carbon number N within a range from 9 to 18, wherein the raw material is subjected to treatment steps including pretreatment, fraction cutting, alkane-alkene separation, and isomer separation, thereby obtaining a high purity ?-olefin product. As compared to conventional rectification and extraction processes, the product obtained by the method of the present disclosure has advantages of higher purity, higher yield, lower energy consumption, and significantly reduced production cost.
    Type: Grant
    Filed: June 14, 2019
    Date of Patent: June 28, 2022
    Assignee: INNER MONGOLIA YITAI COAL-BASED NEW MATERIALS RESEARCH INSTITUTE CO., LTD.
    Inventors: Juncheng Li, Zhen Qian, Jingwei Wu, Xiaolong Zhang, Qinge Jian, Yuan Gao, Xueting Wu, Haoting Chen
  • Patent number: 11354847
    Abstract: A three-dimensional (3D) object reconstruction neural network system learns to predict a 3D shape representation of an object from a video that includes the object. The 3D reconstruction technique may be used for content creation, such as generation of 3D characters for games, movies, and 3D printing. When 3D characters are generated from video, the content may also include motion of the character, as predicted based on the video. The 3D object construction technique exploits temporal consistency to reconstruct a dynamic 3D representation of the object from an unlabeled video. Specifically, an object in a video has a consistent shape and consistent texture across multiple frames. Texture, base shape, and part correspondence invariance constraints may be applied to fine-tune the neural network system. The reconstruction technique generalizes well—particularly for non-rigid objects.
    Type: Grant
    Filed: July 31, 2020
    Date of Patent: June 7, 2022
    Assignee: NVIDIA Corporation
    Inventors: Xueting Li, Sifei Liu, Kihwan Kim, Shalini De Mello, Jan Kautz
  • Publication number: 20220139037
    Abstract: Apparatuses, systems, and techniques to identify a shape or camera pose of a three-dimensional object from a two-dimensional image of the object. In at least one embodiment, objects are identified in an image using one or more neural networks that have been trained on objects of a similar category and a three-dimensional mesh template.
    Type: Application
    Filed: January 18, 2022
    Publication date: May 5, 2022
    Inventors: Xueting Li, Sifei Liu, Kihwan Kim, Shalini De Mello, Varun Jampani, Jan Kautz
  • Publication number: 20220036635
    Abstract: A three-dimensional (3D) object reconstruction neural network system learns to predict a 3D shape representation of an object from a video that includes the object. The 3D reconstruction technique may be used for content creation, such as generation of 3D characters for games, movies, and 3D printing. When 3D characters are generated from video, the content may also include motion of the character, as predicted based on the video. The 3D object construction technique exploits temporal consistency to reconstruct a dynamic 3D representation of the object from an unlabeled video. Specifically, an object in a video has a consistent shape and consistent texture across multiple frames. Texture, base shape, and part correspondence invariance constraints may be applied to fine-tune the neural network system. The reconstruction technique generalizes well—particularly for non-rigid objects.
    Type: Application
    Filed: July 31, 2020
    Publication date: February 3, 2022
    Inventors: Xueting Li, Sifei Liu, Kihwan Kim, Shalini De Mello, Jan Kautz
  • Patent number: 11238650
    Abstract: Apparatuses, systems, and techniques to identify a shape or camera pose of a three-dimensional object from a two-dimensional image of the object. In at least one embodiment, objects are identified in an image using one or more neural networks that have been trained on objects of a similar category and a three-dimensional mesh template.
    Type: Grant
    Filed: April 15, 2020
    Date of Patent: February 1, 2022
    Assignee: NVIDIA Corporation
    Inventors: Xueting Li, Sifei Liu, Kihwan Kim, Shalini De Mello, Varun Jampani, Jan Kautz
  • Publication number: 20210287430
    Abstract: Apparatuses, systems, and techniques to identify a shape or camera pose of a three-dimensional object from a two-dimensional image of the object. In at least one embodiment, objects are identified in an image using one or more neural networks that have been trained on objects of a similar category and a three-dimensional mesh template.
    Type: Application
    Filed: April 15, 2020
    Publication date: September 16, 2021
    Inventors: Xueting Li, Sifei Liu, Kihwan Kim, Shalini De Mello, Varun Jampani, Jan Kautz