Patents by Inventor Sean Ryan FANELLO
Sean Ryan FANELLO 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).
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Patent number: 11037026Abstract: Values of pixels in an image are mapped to a binary space using a first function that preserves characteristics of values of the pixels. Labels are iteratively assigned to the pixels in the image in parallel based on a second function. The label assigned to each pixel is determined based on values of a set of nearest-neighbor pixels. The first function is trained to map values of pixels in a set of training images to the binary space and the second function is trained to assign labels to the pixels in the set of training images. Considering only the nearest neighbors in the inference scheme results in a computational complexity that is independent of the size of the solution space and produces sufficient approximations of the true distribution when the solution for each pixel is most likely found in a small subset of the set of potential solutions.Type: GrantFiled: January 22, 2020Date of Patent: June 15, 2021Assignee: Google LLCInventors: Sean Ryan Fanello, Julien Pascal Christophe Valentin, Adarsh Prakash Murthy Kowdle, Christoph Rhemann, Vladimir Tankovich, Philip L. Davidson, Shahram Izadi
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Patent number: 10937182Abstract: An electronic device estimates a pose of one or more subjects in an environment based on estimating a correspondence between a data volume containing a data mesh based on a current frame captured by a depth camera and a reference volume containing a plurality of fused prior data frames based on spectral embedding and performing bidirectional non-rigid matching between the reference volume and the current data frame to refine the correspondence so as to support location-based functionality. The electronic device predicts correspondences between the data volume and the reference volume based on spectral embedding. The correspondences provide constraints that accelerate the convergence between the data volume and the reference volume. By tracking changes between the current data mesh frame and the reference volume, the electronic device avoids tracking failures that can occur when relying solely on a previous data mesh frame.Type: GrantFiled: May 31, 2018Date of Patent: March 2, 2021Assignee: GOOGLE LLCInventors: Mingsong Dou, Sean Ryan Fanello, Adarsh Prakash Murthy Kowdle, Christoph Rhemann, Sameh Khamis, Philip L. Davidson, Shahram Izadi, Vladimir Tankovich
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Patent number: 10839539Abstract: An electronic device estimates a depth map of an environment based on stereo depth images captured by depth cameras having exposure times that are offset from each other in conjunction with illuminators pulsing illumination patterns into the environment. A processor of the electronic device matches small sections of the depth images from the cameras to each other and to corresponding patches of immediately preceding depth images (e.g., a spatio-temporal image patch “cube”). The processor computes a matching cost for each spatio-temporal image patch cube by converting each spatio-temporal image patch into binary codes and defining a cost function between two stereo image patches as the difference between the binary codes. The processor minimizes the matching cost to generate a disparity map, and optimizes the disparity map by rejecting outliers using a decision tree with learned pixel offsets and refining subpixels to generate a depth map of the environment.Type: GrantFiled: May 31, 2018Date of Patent: November 17, 2020Assignee: GOOGLE LLCInventors: Adarsh Prakash Murthy Kowdle, Vladimir Tankovich, Danhang Tang, Cem Keskin, Jonathan James Taylor, Philip L. Davidson, Shahram Izadi, Sean Ryan Fanello, Julien Pascal Christophe Valentin, Christoph Rhemann, Mingsong Dou, Sameh Khamis, David Kim
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Publication number: 20200160109Abstract: Values of pixels in an image are mapped to a binary space using a first function that preserves characteristics of values of the pixels. Labels are iteratively assigned to the pixels in the image in parallel based on a second function. The label assigned to each pixel is determined based on values of a set of nearest-neighbor pixels. The first function is trained to map values of pixels in a set of training images to the binary space and the second function is trained to assign labels to the pixels in the set of training images. Considering only the nearest neighbors in the inference scheme results in a computational complexity that is independent of the size of the solution space and produces sufficient approximations of the true distribution when the solution for each pixel is most likely found in a small subset of the set of potential solutions.Type: ApplicationFiled: January 22, 2020Publication date: May 21, 2020Inventors: Sean Ryan FANELLO, Julien Pascal Christophe VALENTIN, Adarsh Prakash Murthy KOWDLE, Christoph RHEMANN, Vladimir TANKOVICH, Philip L. DAVIDSON, Shahram IZADI
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Patent number: 10579905Abstract: Values of pixels in an image are mapped to a binary space using a first function that preserves characteristics of values of the pixels. Labels are iteratively assigned to the pixels in the image in parallel based on a second function. The label assigned to each pixel is determined based on values of a set of nearest-neighbor pixels. The first function is trained to map values of pixels in a set of training images to the binary space and the second function is trained to assign labels to the pixels in the set of training images. Considering only the nearest neighbors in the inference scheme results in a computational complexity that is independent of the size of the solution space and produces sufficient approximations of the true distribution when the solution for each pixel is most likely found in a small subset of the set of potential solutions.Type: GrantFiled: March 19, 2018Date of Patent: March 3, 2020Assignee: GOOGLE LLCInventors: Sean Ryan Fanello, Julien Pascal Christophe Valentin, Adarsh Prakash Murthy Kowdle, Christoph Rhemann, Vladimir Tankovich, Philip L. Davidson, Shahram Izadi
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Patent number: 10554957Abstract: A first and second image of a scene are captured. Each of a plurality of pixels in the first image is associated with a disparity value. An image patch associated with each of the plurality of pixels of the first image and the second image is mapped into a binary vector. Thus, values of pixels in an image are mapped to a binary space using a function that preserves characteristics of values of the pixels. The difference between the binary vector associated with each of the plurality of pixels of the first image and its corresponding binary vector in the second image designated by the disparity value associated with each of the plurality of pixels of the first image is determined. Based on the determined difference between binary vectors, correspondence between the plurality of pixels of the first image and the second image is established.Type: GrantFiled: June 4, 2018Date of Patent: February 4, 2020Assignee: GOOGLE LLCInventors: Julien Pascal Christophe Valentin, Sean Ryan Fanello, Adarsh Prakash Murthy Kowdle, Christoph Rhemann, Vladimir Tankovich, Philip L. Davidson, Shahram Izadi
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Publication number: 20180350087Abstract: An electronic device estimates a depth map of an environment based on stereo depth images captured by depth cameras having exposure times that are offset from each other in conjunction with illuminators pulsing illumination patterns into the environment. A processor of the electronic device matches small sections of the depth images from the cameras to each other and to corresponding patches of immediately preceding depth images (e.g., a spatio-temporal image patch “cube”). The processor computes a matching cost for each spatio-temporal image patch cube by converting each spatio-temporal image patch into binary codes and defining a cost function between two stereo image patches as the difference between the binary codes. The processor minimizes the matching cost to generate a disparity map, and optimizes the disparity map by rejecting outliers using a decision tree with learned pixel offsets and refining subpixels to generate a depth map of the environment.Type: ApplicationFiled: May 31, 2018Publication date: December 6, 2018Inventors: Adarsh Prakash Murthy KOWDLE, Vladimir TANKOVICH, Danhang TANG, Cem KESKIN, Jonathan James Taylor, Philip L. DAVIDSON, Shahram IZADI, Sean Ryan FANELLO, Julien Pascal Christophe VALENTIN, Christoph RHEMANN, Mingsong DOU, Sameh KHAMIS, David KIM
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Publication number: 20180350088Abstract: An electronic device estimates a pose of one or more subjects in an environment based on estimating a correspondence between a data volume containing a data mesh based on a current frame captured by a depth camera and a reference volume containing a plurality of fused prior data frames based on spectral embedding and performing bidirectional non-rigid matching between the reference volume and the current data frame to refine the correspondence so as to support location-based functionality. The electronic device predicts correspondences between the data volume and the reference volume based on spectral embedding. The correspondences provide constraints that accelerate the convergence between the data volume and the reference volume. By tracking changes between the current data mesh frame and the reference volume, the electronic device avoids tracking failures that can occur when relying solely on a previous data mesh frame.Type: ApplicationFiled: May 31, 2018Publication date: December 6, 2018Inventors: Mingsong DOU, Sean Ryan FANELLO, Adarsh Prakash Murthy KOWDLE, Christoph RHEMANN, Sameh KHAMIS, Philip L. DAVIDSON, Shahram IZADI, Vladimir Tankovich
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Publication number: 20180352213Abstract: A first and second image of a scene are captured. Each of a plurality of pixels in the first image is associated with a disparity value. An image patch associated with each of the plurality of pixels of the first image and the second image is mapped into a binary vector. Thus, values of pixels in an image are mapped to a binary space using a function that preserves characteristics of values of the pixels. The difference between the binary vector associated with each of the plurality of pixels of the first image and its corresponding binary vector in the second image designated by the disparity value associated with each of the plurality of pixels of the first image is determined. Based on the determined difference between binary vectors, correspondence between the plurality of pixels of the first image and the second image is established.Type: ApplicationFiled: June 4, 2018Publication date: December 6, 2018Inventors: Julien Pascal Christophe Valentin, Sean Ryan Fanello, Adarsh Prakash Murthy Kowdle, Christoph Rhemann, Vladimir Tankovich, Philip L. Davidson, Shahram Izadi
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Publication number: 20180300588Abstract: Values of pixels in an image are mapped to a binary space using a first function that preserves characteristics of values of the pixels. Labels are iteratively assigned to the pixels in the image in parallel based on a second function. The label assigned to each pixel is determined based on values of a set of nearest-neighbor pixels. The first function is trained to map values of pixels in a set of training images to the binary space and the second function is trained to assign labels to the pixels in the set of training images. Considering only the nearest neighbors in the inference scheme results in a computational complexity that is independent of the size of the solution space and produces sufficient approximations of the true distribution when the solution for each pixel is most likely found in a small subset of the set of potential solutions.Type: ApplicationFiled: March 19, 2018Publication date: October 18, 2018Inventors: Sean Ryan FANELLO, Julien Pascal Christophe VALENTIN, Adarsh Prakash Murthy KOWDLE, Christoph RHEMANN, Vladimir TANKOVICH, Philip L. DAVIDSON, Shahram IZADI