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    TRAINING A NEURAL NETWORK TO PREDICT SUPERPIXELS USING SEGMENTATION-AWARE AFFINITY LOSS
    3.
    发明申请
    TRAINING A NEURAL NETWORK TO PREDICT SUPERPIXELS USING SEGMENTATION-AWARE AFFINITY LOSS 审中-公开

    公开(公告)号：US20200334502A1

    公开(公告)日：2020-10-22

    申请号：US16921012

    申请日：2020-07-06

    Applicant: NVIDIA Corporation

    Inventor： Wei-Chih Tu ,  Ming-Yu Liu ,  Varun Jampani ,  Deqing Sun ,  Ming-Hsuan Yang ,  Jan Kautz

    IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06T7/11

    Abstract: Segmentation is the identification of separate objects within an image. An example is identification of a pedestrian passing in front of a car, where the pedestrian is a first object and the car is a second object. Superpixel segmentation is the identification of regions of pixels within an object that have similar properties. An example is identification of pixel regions having a similar color, such as different articles of clothing worn by the pedestrian and different components of the car. A pixel affinity neural network (PAN) model is trained to generate pixel affinity maps for superpixel segmentation. The pixel affinity map defines the similarity of two points in space. In an embodiment, the pixel affinity map indicates a horizontal affinity and vertical affinity for each pixel in the image. The pixel affinity map is processed to identify the superpixels.

    Training a neural network to predict superpixels using segmentation-aware affinity loss
    4.
    发明授权
    Training a neural network to predict superpixels using segmentation-aware affinity loss 有权

    公开(公告)号：US10748036B2

    公开(公告)日：2020-08-18

    申请号：US16188641

    申请日：2018-11-13

    Applicant: NVIDIA Corporation

    Inventor： Wei-Chih Tu ,  Ming-Yu Liu ,  Varun Jampani ,  Deqing Sun ,  Ming-Hsuan Yang ,  Jan Kautz

    IPC: G06K9/00 ,  G06T7/00 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06T7/11

    Abstract: Segmentation is the identification of separate objects within an image. An example is identification of a pedestrian passing in front of a car, where the pedestrian is a first object and the car is a second object. Superpixel segmentation is the identification of regions of pixels within an object that have similar properties An example is identification of pixel regions having a similar color, such as different articles of clothing worn by the pedestrian and different components of the car. A pixel affinity neural network (PAN) model is trained to generate pixel affinity maps for superpixel segmentation. The pixel affinity map defines the similarity of two points in space. In an embodiment, the pixel affinity map indicates a horizontal affinity and vertical affinity for each pixel in the image. The pixel affinity map is processed to identify the superpixels.

    Scene flow estimation using shared features
    7.
    发明授权
    Scene flow estimation using shared features 有权

    公开(公告)号：US10986325B2

    公开(公告)日：2021-04-20

    申请号：US16569104

    申请日：2019-09-12

    Applicant: NVIDIA Corporation

    Inventor： Deqing Sun ,  Varun Jampani ,  Erik Gundersen Learned-Miller ,  Huaizu Jiang

    IPC: H04N13/122 ,  H04N13/128 ,  G06N3/08 ,  H04N13/00

    Abstract: Scene flow represents the three-dimensional (3D) structure and movement of objects in a video sequence in three dimensions from frame-to-frame and is used to track objects and estimate speeds for autonomous driving applications. Scene flow is recovered by a neural network system from a video sequence captured from at least two viewpoints (e.g., cameras), such as a left-eye and right-eye of a viewer. An encoder portion of the system extracts features from frames of the video sequence. The features are input to a first decoder to predict optical flow and a second decoder to predict disparity. The optical flow represents pixel movement in (x,y) and the disparity represents pixel movement in z (depth). When combined, the optical flow and disparity represent the scene flow.

    VIEW SYNTHESIS USING NEURAL NETWORKS
    8.
    发明申请
    VIEW SYNTHESIS USING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200294194A1

    公开(公告)日：2020-09-17

    申请号：US16299062

    申请日：2019-03-11

    Applicant: NVIDIA Corporation

    Inventor： Deqing Sun ,  Orazio Gallo ,  Jan Kautz ,  Jinwei GU ,  Wei-Sheng Lai

    IPC: G06T3/40 ,  G06K9/34 ,  G06K9/00 ,  G06K9/32 ,  G06N3/08

    Abstract: A video stitching system combines video from different cameras to form a panoramic video that, in various embodiments, is temporally stable and tolerant to strong parallax. In an embodiment, the system provides a smooth spatial interpolation that can be used to connect the input video images. In an embodiment, the system applies an interpolation layer to slices of the overlapping video sources, and the network learns a dense flow field to smoothly align the input videos with spatial interpolation. Various embodiments are applicable to areas such as virtual reality, immersive telepresence, autonomous driving, and video surveillance.

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