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公开(公告)号：US12190438B2
公开(公告)日：2025-01-07
申请号：US18400289
申请日：2023-12-29
Applicant: Snap Inc.
Inventor： Kai Zhou , Qi Qi , Jeroen Hol
Abstract: Systems and methods of generating ground truth datasets for producing virtual reality (VR) experiences, for testing simulated sensor configurations, and for training machine-learning algorithms. In one example, a recording device with one or more cameras and one or more inertial measurement units captures images and motion data along a real path through a physical environment. A SLAM application uses the captured data to calculate the trajectory of the recording device. A polynomial interpolation module uses Chebyshev polynomials to generate a continuous time trajectory (CTT) function. The method includes identifying a virtual environment and assembling a simulated sensor configuration, such as a VR headset. Using the CTT function, the method includes generating a ground truth output dataset that represents the simulated sensor configuration in motion along a virtual path through the virtual environment. The virtual path is closely correlated with the motion along the real path as captured by the recording device. Accordingly, the output dataset produces a realistic and life-like VR experience. In addition, the methods described can be used to generate multiple output datasets, at various sample rates, which are useful for training the machine-learning algorithms which are part of many VR systems.
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公开(公告)号：US20220366639A1
公开(公告)日：2022-11-17
申请号：US17877451
申请日：2022-07-29
Applicant: Snap Inc.
Inventor： Kai Zhou , Qi Qi , Jeroen Hol
Abstract: Systems and methods of generating ground truth datasets for producing virtual reality (VR) experiences, for testing simulated sensor configurations, and for training machine-learning algorithms. In one example, a recording device with one or more cameras and one or more inertial measurement units captures images and motion data along a real path through a physical environment. A SLAM application uses the captured data to calculate the trajectory of the recording device. A polynomial interpolation module uses Chebyshev polynomials to generate a continuous time trajectory (CTT) function. The method includes identifying a virtual environment and assembling a simulated sensor configuration, such as a VR headset. Using the CTT function, the method includes generating a ground truth output dataset that represents the simulated sensor configuration in motion along a virtual path through the virtual environment. The virtual path is closely correlated with the motion along the real path as captured by the recording device. Accordingly, the output dataset produces a realistic and life-like VR experience. In addition, the methods described can be used to generate multiple output datasets, at various sample rates, which are useful for training the machine-learning algorithms which are part of many VR systems.
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Patent Agency Ranking
公开(公告)号：US11887246B2
公开(公告)日：2024-01-30
申请号：US17877451
申请日：2022-07-29
Applicant: Snap Inc.
Inventor： Kai Zhou , Qi Qi , Jeroen Hol
CPC classification number: G06T15/20 , G06T7/20 , G06T7/50 , G06T7/70 , G06T2207/30241 , G06T2207/30244
Abstract: Systems and methods of generating ground truth datasets for producing virtual reality (VR) experiences, for testing simulated sensor configurations, and for training machine-learning algorithms. In one example, a recording device with one or more cameras and one or more inertial measurement units captures images and motion data along a real path through a physical environment. A SLAM application uses the captured data to calculate the trajectory of the recording device. A polynomial interpolation module uses Chebyshev polynomials to generate a continuous time trajectory (CTT) function. The method includes identifying a virtual environment and assembling a simulated sensor configuration, such as a VR headset. Using the CTT function, the method includes generating a ground truth output dataset that represents the simulated sensor configuration in motion along a virtual path through the virtual environment. The virtual path is closely correlated with the motion along the real path as captured by the recording device. Accordingly, the output dataset produces a realistic and life-like VR experience. In addition, the methods described can be used to generate multiple output datasets, at various sample rates, which are useful for training the machine-learning algorithms which are part of many VR systems.
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公开(公告)号：US11417052B2
公开(公告)日：2022-08-16
申请号：US17342851
申请日：2021-06-09
Applicant: Snap Inc.
Inventor： Kai Zhou , Qi Qi , Jeroen Hol
Abstract: Systems and methods of generating ground truth datasets for producing virtual reality (VR) experiences, for testing simulated sensor configurations, and for training machine-learning algorithms. In one example, a recording device with one or more cameras and one or more inertial measurement units captures images and motion data along a real path through a physical environment. A SLAM application uses the captured data to calculate the trajectory of the recording device. A polynomial interpolation module uses Chebyshev polynomials to generate a continuous time trajectory (CTT) function. The method includes identifying a virtual environment and assembling a simulated sensor configuration, such as a VR headset. Using the CTT function, the method includes generating a ground truth output dataset that represents the simulated sensor configuration in motion along a virtual path through the virtual environment. The virtual path is closely correlated with the motion along the real path as captured by the recording device. Accordingly, the output dataset produces a realistic and life-like VR experience. In addition, the methods described can be used to generate multiple output datasets, at various sample rates, which are useful for training the machine-learning algorithms which are part of many VR systems.
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公开(公告)号：US20210407178A1
公开(公告)日：2021-12-30
申请号：US17342851
申请日：2021-06-09
Applicant: Snap Inc.
Inventor： Kai Zhou , Qi Qi , Jeroen Hol
Abstract: Systems and methods of generating ground truth datasets for producing virtual reality (VR) experiences, for testing simulated sensor configurations, and for training machine-learning algorithms. In one example, a recording device with one or more cameras and one or more inertial measurement units captures images and motion data along a real path through a physical environment. A SLAM application uses the captured data to calculate the trajectory of the recording device. A polynomial interpolation module uses Chebyshev polynomials to generate a continuous time trajectory (CTT) function. The method includes identifying a virtual environment and assembling a simulated sensor configuration, such as a VR headset. Using the CTT function, the method includes generating a ground truth output dataset that represents the simulated sensor configuration in motion along a virtual path through the virtual environment. The virtual path is closely correlated with the motion along the real path as captured by the recording device. Accordingly, the output dataset produces a realistic and life-like VR experience. In addition, the methods described can be used to generate multiple output datasets, at various sample rates, which are useful for training the machine-learning algorithms which are part of many VR systems.
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公开(公告)号：US20250095282A1
公开(公告)日：2025-03-20
申请号：US18968137
申请日：2024-12-04
Applicant: Snap Inc.
Inventor： Kai Zhou , Qi Qi , Jeroen Hol
Abstract: Systems and methods of generating ground truth datasets for producing virtual reality (VR) experiences, for testing simulated sensor configurations, and for training machine-learning algorithms. In one example, a recording device with one or more cameras and one or more inertial measurement units captures images and motion data along a real path through a physical environment. A SLAM application uses the captured data to calculate the trajectory of the recording device. A polynomial interpolation module uses Chebyshev polynomials to generate a continuous time trajectory (CTT) function. The method includes identifying a virtual environment and assembling a simulated sensor configuration, such as a VR headset. Using the CTT function, the method includes generating a ground truth output dataset that represents the simulated sensor configuration in motion along a virtual path through the virtual environment. The virtual path is closely correlated with the motion along the real path as captured by the recording device. Accordingly, the output dataset produces a realistic and life-like VR experience. In addition, the methods described can be used to generate multiple output datasets, at various sample rates, which are useful for training the machine-learning algorithms which are part of many VR systems.
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公开(公告)号：US20240135633A1
公开(公告)日：2024-04-25
申请号：US18400289
申请日：2023-12-29
Applicant: Snap Inc.
Inventor： Kai Zhou , Qi Qi , Jeroen Hol
CPC classification number: G06T15/20 , G06T7/20 , G06T7/50 , G06T7/70 , G06T2207/30241 , G06T2207/30244
Abstract: Systems and methods of generating ground truth datasets for producing virtual reality (VR) experiences, for testing simulated sensor configurations, and for training machine-learning algorithms. In one example, a recording device with one or more cameras and one or more inertial measurement units captures images and motion data along a real path through a physical environment. A SLAM application uses the captured data to calculate the trajectory of the recording device. A polynomial interpolation module uses Chebyshev polynomials to generate a continuous time trajectory (CTT) function. The method includes identifying a virtual environment and assembling a simulated sensor configuration, such as a VR headset. Using the CTT function, the method includes generating a ground truth output dataset that represents the simulated sensor configuration in motion along a virtual path through the virtual environment. The virtual path is closely correlated with the motion along the real path as captured by the recording device. Accordingly, the output dataset produces a realistic and life-like VR experience. In addition, the methods described can be used to generate multiple output datasets, at various sample rates, which are useful for training the machine-learning algorithms which are part of many VR systems.
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