MOTION-AWARE NEURAL RADIANCE FIELD NETWORK TRAINING

    公开(公告)号:US20240420341A1

    公开(公告)日:2024-12-19

    申请号:US18334931

    申请日:2023-06-14

    Abstract: A method includes obtaining multiple training image frames of a scene, where the training image frames are captured at multiple viewpoints and multiple viewing angles relative to the scene. The method also includes generating multiple initial motion maps using the training image frames and identifying three-dimensional (3D) feature points associated with the scene using the training image frames. The method further includes generating tuned motion masks using the initial motion maps and projections of the 3D feature points onto the initial motion maps. In addition, the method includes training a machine learning model using the training image frames and the tuned motion masks, where the machine learning model is trained to generate 3D information about the scene from viewpoints and viewing angles not captured in the training image frames.

    MULTI-STAGE MULTI-FRAME DENOISING WITH NEURAL RADIANCE FIELD NETWORKS OR OTHER MACHINE LEARNING MODELS

    公开(公告)号:US20250022098A1

    公开(公告)日:2025-01-16

    申请号:US18350558

    申请日:2023-07-11

    Abstract: A method includes obtaining, using at least one processing device of an electronic device, raw image frames of a scene. The raw image frames include different sets of raw image frames captured at different viewpoints and different viewing angles relative to the scene. The method also includes performing, using the at least one processing device, blending of each set of raw image frames in order to generate blended image frames of the scene. The method further includes training, using the at least one processing device, a machine learning model using the blended image frames. The machine learning model is trained to generate three-dimensional (3D) information about the scene from viewpoints and viewing angles not captured in the sets of raw image frames.

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