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    Shape fusion for image analysis
    1.
    发明授权
    Shape fusion for image analysis 有权

    公开(公告)号：US12141986B2

    公开(公告)日：2024-11-12

    申请号：US18333166

    申请日：2023-06-12

    Applicant: Nvidia Corporation

    Inventor： David Jesus Acuna Marrero ,  Towaki Takikawa ,  Varun Jampani ,  Sanja Fidler

    IPC: G06T7/12 ,  G06F18/25 ,  G06V10/20 ,  G06V10/44 ,  G06V10/764 ,  G06V10/80 ,  G06V10/82 ,  G06V20/56

    Abstract: Various types of image analysis benefit from a multi-stream architecture that allows the analysis to consider shape data. A shape stream can process image data in parallel with a primary stream, where data from layers of a network in the primary stream is provided as input to a network of the shape stream. The shape data can be fused with the primary analysis data to produce more accurate output, such as to produce accurate boundary information when the shape data is used with semantic segmentation data produced by the primary stream. A gate structure can be used to connect the intermediate layers of the primary and shape streams, using higher level activations to gate lower level activations in the shape stream. Such a gate structure can help focus the shape stream on the relevant information and reduces any additional weight of the shape stream.

    Switchable propagation neural network
    3.
    发明授权
    Switchable propagation neural network 有权

    公开(公告)号：US11328173B2

    公开(公告)日：2022-05-10

    申请号：US17081805

    申请日：2020-10-27

    Applicant: NVIDIA Corporation

    Inventor： Sifei Liu ,  Shalini De Mello ,  Jinwei Gu ,  Varun Jampani ,  Jan Kautz

    IPC: G06K9/62 ,  G06T7/90 ,  G06T5/00 ,  G06T7/10 ,  G06N3/08 ,  G06N3/04 ,  G06T5/50 ,  G06V20/40

    Abstract: A temporal propagation network (TPN) system learns the affinity matrix for video image processing tasks. An affinity matrix is a generic matrix that defines the similarity of two points in space. The TPN system includes a guidance neural network model and a temporal propagation module and is trained for a particular computer vision task to propagate visual properties from a key-frame represented by dense data (color), to another frame that is represented by coarse data (grey-scale). The guidance neural network model generates an affinity matrix referred to as a global transformation matrix from task-specific data for the key-frame and the other frame. The temporal propagation module applies the global transformation matrix to the key-frame property data to produce propagated property data (color) for the other frame. For example, the TPN system may be used to colorize several frames of greyscale video using a single manually colorized key-frame.

    TRAINING A NEURAL NETWORK TO PREDICT SUPERPIXELS USING SEGMENTATION-AWARE AFFINITY LOSS
    4.
    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
    5.
    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
    10.
    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.

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