Patents by Inventor David Alan Gausebeck

David Alan Gausebeck 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: 11164394
    Abstract: The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises receiving, by a system operatively coupled to a processor, a two-dimensional image, and determining, by the system, auxiliary data for the two-dimensional image, wherein the auxiliary data comprises orientation information regarding a capture orientation of the two-dimensional image. The method further comprises, deriving, by the system, three-dimensional information for the two-dimensional image using one or more neural network models configured to infer the three-dimensional information based on the two-dimensional image and the auxiliary data.
    Type: Grant
    Filed: September 25, 2018
    Date of Patent: November 2, 2021
    Assignee: Matterport, Inc.
    Inventor: David Alan Gausebeck
  • Patent number: 11094137
    Abstract: The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a system is described comprising a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a reception component configured to receive two-dimensional images, and a three-dimensional data derivation component configured to employ one or more three-dimensional data from two-dimensional data (3D-from-2D) neural network models to derive three-dimensional data for the two-dimensional images.
    Type: Grant
    Filed: September 25, 2018
    Date of Patent: August 17, 2021
    Assignee: Matterport, Inc.
    Inventors: David Alan Gausebeck, Matthew Tschudy Bell, Waleed K. Abdulla, Peter Kyuhee Hahn
  • Patent number: 11094117
    Abstract: Systems and techniques for processing and/or transmitting three-dimensional (3D) data are presented. A partitioning component receives captured 3D data associated with a 3D model of an interior environment and partitions the captured 3D data into at least one data chunk associated with at least a first level of detail and a second level of detail. A data component stores 3D data including at least the first level of detail and the second level of detail for the at least one data chunk. An output component transmits a portion of data from the at least one data chunk that is associated with the first level of detail or the second level of detail to a remote client device based on information associated with the first level of detail and the second level of detail.
    Type: Grant
    Filed: March 10, 2020
    Date of Patent: August 17, 2021
    Assignee: Matterport, Inc.
    Inventors: Matthew Tschudy Bell, David Alan Gausebeck, Gregory William Coombe, Daniel Ford
  • Patent number: 11069117
    Abstract: Systems and methods for generating three-dimensional models having regions of various resolutions are provided. In particular, imagery data can be captured and utilized to generate three-dimensional models. Regions of texture can be mapped to regions of a three-dimensional model when rendered. Resolutions of texture can be selectively altered and regions of texture can be selectively segmented to reduce texture memory cost. Texture can be algorithmically generated based on alternative texturing techniques. Models can be rendered having regions at various resolutions.
    Type: Grant
    Filed: May 6, 2019
    Date of Patent: July 20, 2021
    Assignee: Matterport, Inc.
    Inventors: Daniel Ford, Matthew Tschudy Bell, David Alan Gausebeck, Mykhaylo Kurinnyy
  • Publication number: 20210158618
    Abstract: Systems and methods for generating three-dimensional models with correlated three-dimensional and two dimensional imagery data are provided. In particular, imagery data can be captured in two dimensions and three dimensions. Imagery data can be transformed into models. Two-dimensional data and three-dimensional data can be correlated within models. Two-dimensional data can be selected for display within a three-dimensional model. Modifications can be made to the three-dimensional model and can be displayed within a three-dimensional model or within two-dimensional data. Models can transition between two dimensional imagery data and three dimensional imagery data.
    Type: Application
    Filed: February 2, 2021
    Publication date: May 27, 2021
    Inventors: Matthew Tschudy Bell, David Alan Gausebeck, Gregory William Coombe, Daniel Ford, William John Brown
  • Publication number: 20210141965
    Abstract: Systems and techniques for processing three-dimensional (3D) data are presented. Captured three-dimensional (3D) data associated with a 3D model of an architectural environment is received and at least a portion of the captured 3D data associated with a flat surface is identified. Furthermore, missing data associated with the portion of the captured 3D data is identified and additional 3D data for the missing data is generated based on other data associated with the portion of the captured 3D data.
    Type: Application
    Filed: October 13, 2020
    Publication date: May 13, 2021
    Inventors: Matthew Tschudy Bell, David Alan Gausebeck, Daniel Ford, Gregory William Coombe
  • Patent number: 10909758
    Abstract: Systems and methods for generating three-dimensional models with correlated three-dimensional and two dimensional imagery data are provided. In particular, imagery data can be captured in two dimensions and three dimensions. Imagery data can be transformed into models. Two-dimensional data and three-dimensional data can be correlated within models. Two-dimensional data can be selected for display within a three-dimensional model. Modifications can be made to the three-dimensional model and can be displayed within a three-dimensional model or within two-dimensional data. Models can transition between two dimensional imagery data and three dimensional imagery data.
    Type: Grant
    Filed: October 9, 2018
    Date of Patent: February 2, 2021
    Assignee: Matterport, Inc.
    Inventors: Matthew Tschudy Bell, David Alan Gausebeck, Gregory William Coombe, Daniel Ford, William John Brown
  • Publication number: 20200388072
    Abstract: Systems and techniques for processing and/or transmitting three-dimensional (3D) data are presented. A partitioning component receives captured 3D data associated with a 3D model of an interior environment and partitions the captured 3D data into at least one data chunk associated with at least a first level of detail and a second level of detail. A data component stores 3D data including at least the first level of detail and the second level of detail for the at least one data chunk. An output component transmits a portion of data from the at least one data chunk that is associated with the first level of detail or the second level of detail to a remote client device based on information associated with the first level of detail and the second level of detail.
    Type: Application
    Filed: March 10, 2020
    Publication date: December 10, 2020
    Applicant: Matterport, Inc.
    Inventors: Matthew Tschudy Bell, David Alan Gausebeck, Gregory William Coombe, Daniel Ford
  • Patent number: 10803208
    Abstract: Systems and techniques for processing three-dimensional (3D) data are presented. Captured three-dimensional (3D) data associated with a 3D model of an architectural environment is received and at least a portion of the captured 3D data associated with a flat surface is identified. Furthermore, missing data associated with the portion of the captured 3D data is identified and additional 3D data for the missing data is generated based on other data associated with the portion of the captured 3D data.
    Type: Grant
    Filed: March 16, 2018
    Date of Patent: October 13, 2020
    Assignee: Matterport, Inc.
    Inventors: Matthew Tschudy Bell, David Alan Gausebeck, Daniel Ford, Gregory William Coombe
  • Patent number: 10706615
    Abstract: Systems and techniques for determining and/or generating data for an architectural opening area associated with a three-dimensional (3D) model are presented. A portion of an image associated with a 3D model that corresponds to a window view or another architectural opening area is identified based at least in part on color data or depth data. Furthermore, a surface associated with the 3D model and visual data for the window view or the other architectural opening area is determined. The visual data for the window view or the other architectural opening area is applied to the surface associated with the 3D model.
    Type: Grant
    Filed: December 8, 2015
    Date of Patent: July 7, 2020
    Assignee: Matterport, Inc.
    Inventors: Daniel Ford, David Alan Gausebeck, Gunnar Hovden, Matthew Tschudy Bell
  • Patent number: 10586386
    Abstract: Systems and techniques for processing and/or transmitting three-dimensional (3D) data are presented. A partitioning component receives captured 3D data associated with a 3D model of an interior environment and partitions the captured 3D data into at least one data chunk associated with at least a first level of detail and a second level of detail. A data component stores 3D data including at least the first level of detail and the second level of detail for the at least one data chunk. An output component transmits a portion of data from the at least one data chunk that is associated with the first level of detail or the second level of detail to a remote client device based on information associated with the first level of detail and the second level of detail.
    Type: Grant
    Filed: June 13, 2018
    Date of Patent: March 10, 2020
    Assignee: Matterport, Inc.
    Inventors: Matthew Tschudy Bell, David Alan Gausebeck, Gregory William Coombe, Daniel Ford
  • Publication number: 20190259194
    Abstract: Systems and methods for generating three-dimensional models having regions of various resolutions are provided. In particular, imagery data can be captured and utilized to generate three-dimensional models. Regions of texture can be mapped to regions of a three-dimensional model when rendered. Resolutions of texture can be selectively altered and regions of texture can be selectively segmented to reduce texture memory cost. Texture can be algorithmically generated based on alternative texturing techniques. Models can be rendered having regions at various resolutions.
    Type: Application
    Filed: May 6, 2019
    Publication date: August 22, 2019
    Inventors: Daniel Ford, Matthew Tschudy Bell, David Alan Gausebeck, Mykhaylo Kurinnyy
  • Patent number: 10325399
    Abstract: Systems and methods for generating three-dimensional models having regions of various resolutions are provided. In particular, imagery data can be captured and utilized to generate three-dimensional models. Regions of texture can be mapped to regions of a three-dimensional model when rendered. Resolutions of texture can be selectively altered and regions of texture can be selectively segmented to reduce texture memory cost. Texture can be algorithmically generated based on alternative texturing techniques. Models can be rendered having regions at various resolutions.
    Type: Grant
    Filed: June 22, 2018
    Date of Patent: June 18, 2019
    Assignee: Matterport, Inc.
    Inventors: Daniel Ford, Matthew Tschudy Bell, David Alan Gausebeck, Mykhaylo Kurinnyy
  • Publication number: 20190051050
    Abstract: Systems and methods for generating three-dimensional models with correlated three-dimensional and two dimensional imagery data are provided. In particular, imagery data can be captured in two dimensions and three dimensions. Imagery data can be transformed into models. Two-dimensional data and three-dimensional data can be correlated within models. Two-dimensional data can be selected for display within a three-dimensional model. Modifications can be made to the three-dimensional model and can be displayed within a three-dimensional model or within two-dimensional data. Models can transition between two dimensional imagery data and three dimensional imagery data.
    Type: Application
    Filed: October 9, 2018
    Publication date: February 14, 2019
    Inventors: Matthew Tschudy Bell, David Alan Gausebeck, Gregory William Coombe, Daniel Ford, William John Brown
  • Publication number: 20190035165
    Abstract: The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises receiving, by a system operatively coupled to a processor, a two-dimensional image, and determining, by the system, auxiliary data for the two-dimensional image, wherein the auxiliary data comprises orientation information regarding a capture orientation of the two-dimensional image.
    Type: Application
    Filed: September 25, 2018
    Publication date: January 31, 2019
    Inventor: David Alan Gausebeck
  • Publication number: 20190026958
    Abstract: The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises employing, by a system comprising a processor, one or more three-dimensional data from two-dimensional data (3D-from-2D) neural network models to derive three-dimensional data from one or more two-dimensional images captured of an object or environment from a current perspective of the object or environment viewed on or through a display of the device. The method further comprises, determining, by the system, a position for integrating a graphical data object on or within a representation of the object or environment viewed on or through the display based on the current perspective and the three-dimensional data.
    Type: Application
    Filed: September 25, 2018
    Publication date: January 24, 2019
    Inventors: David Alan Gausebeck, Babak Robert Shakib
  • Publication number: 20190026956
    Abstract: The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a system is described comprising a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a reception component configured to receive two-dimensional images, and a three-dimensional data derivation component configured to employ one or more three-dimensional data from two-dimensional data (3D-from-2D) neural network models to derive three-dimensional data for the two-dimensional images.
    Type: Application
    Filed: September 25, 2018
    Publication date: January 24, 2019
    Inventors: David Alan Gausebeck, Matthew Tschudy Bell, Waleed K. Abdulla, Peter Kyuhee Hahn
  • Publication number: 20190026957
    Abstract: The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises receiving, by a system comprising a processor, a panoramic image, and employing, by the system, a three-dimensional data from two-dimensional data (3D-from-2D) convolutional neural network model to derive three-dimensional data from the panoramic image, wherein the 3D-from-2D convolutional neural network model employs convolutional layers that wrap around the panoramic image as projected on a two-dimensional plane to facilitate deriving the three-dimensional data.
    Type: Application
    Filed: September 25, 2018
    Publication date: January 24, 2019
    Inventor: David Alan Gausebeck
  • Patent number: 10163261
    Abstract: Systems and methods for generating three-dimensional models with correlated three-dimensional and two dimensional imagery data are provided. In particular, imagery data can be captured in two dimensions and three dimensions. Imagery data can be transformed into models. Two-dimensional data and three-dimensional data can be correlated within models. Two-dimensional data can be selected for display within a three-dimensional model. Modifications can be made to the three-dimensional model and can be displayed within a three-dimensional model or within two-dimensional data. Models can transition between two dimensional imagery data and three dimensional imagery data.
    Type: Grant
    Filed: March 19, 2014
    Date of Patent: December 25, 2018
    Assignee: Matterport, Inc.
    Inventors: Matthew Tschudy Bell, David Alan Gausebeck, Gregory William Coombe, Daniel Ford, William John Brown
  • Publication number: 20180300936
    Abstract: Systems and methods for generating three-dimensional models having regions of various resolutions are provided. In particular, imagery data can be captured and utilized to generate three-dimensional models. Regions of texture can be mapped to regions of a three-dimensional model when rendered. Resolutions of texture can be selectively altered and regions of texture can be selectively segmented to reduce texture memory cost. Texture can be algorithmically generated based on alternative texturing techniques. Models can be rendered having regions at various resolutions.
    Type: Application
    Filed: June 22, 2018
    Publication date: October 18, 2018
    Inventors: Daniel Ford, Matthew Tschudy Bell, David Alan Gausebeck, Mykhaylo Kurinnyy