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    FAST MULTI-SCALE POINT CLOUD REGISTRATION WITH A HIERARCHICAL GAUSSIAN MIXTURE
    91.
    发明申请
    FAST MULTI-SCALE POINT CLOUD REGISTRATION WITH A HIERARCHICAL GAUSSIAN MIXTURE 审中-公开

    公开(公告)号：US20190319851A1

    公开(公告)日：2019-10-17

    申请号：US16351312

    申请日：2019-03-12

    Applicant: NVIDIA Corporation

    Inventor： Benjamin David Eckart ,  Kihwan Kim ,  Jan Kautz

    IPC: H04L12/24 ,  H04L29/08 ,  G06F16/22

    Abstract: Point cloud registration sits at the core of many important and challenging 3D perception problems including autonomous navigation, object/scene recognition, and augmented reality (AR). A new registration algorithm is presented that achieves speed and accuracy by registering a point cloud to a representation of a reference point cloud. A target point cloud is registered to the reference point cloud by iterating through a number of cycles of an EM algorithm where, during an Expectation step, each point in the target point cloud is associated with a node of a hierarchical tree data structure and, during a Maximization step, an estimated transformation is determined based on the association of the points with corresponding nodes of the hierarchical tree data structure. The estimated transformation is determined by solving a minimization problem associated with a sum, over a number of mixture components, over terms related to a Mahalanobis distance.

    System and method for optical flow estimation
    92.
    发明授权
    System and method for optical flow estimation 有权

    公开(公告)号：US10424069B2

    公开(公告)日：2019-09-24

    申请号：US15942213

    申请日：2018-03-30

    Applicant: NVIDIA Corporation

    Inventor： Deqing Sun ,  Xiaodong Yang ,  Ming-Yu Liu ,  Jan Kautz

    IPC: G06T7/207 ,  G06N5/04 ,  G06T3/00 ,  G06T7/00 ,  G06T7/246 ,  G06N3/04 ,  G06N3/08

    Abstract: A method, computer readable medium, and system are disclosed for estimating optical flow between two images. A first pyramidal set of features is generated for a first image and a partial cost volume for a level of the first pyramidal set of features is computed, by a neural network, using features at the level of the first pyramidal set of features and warped features extracted from a second image, where the partial cost volume is computed across a limited range of pixels that is less than a full resolution of the first image, in pixels, at the level. The neural network processes the features and the partial cost volume to produce a refined optical flow estimate for the first image and the second image.

    Domain Stylization Using a Neural Network Model
    93.
    发明申请
    Domain Stylization Using a Neural Network Model 审中-公开

    公开(公告)号：US20190244060A1

    公开(公告)日：2019-08-08

    申请号：US16265725

    申请日：2019-02-01

    Applicant: NVIDIA Corporation

    Inventor： Aysegul Dundar ,  Ming-Yu Liu ,  Ting-Chun Wang ,  John Zedlewski ,  Jan Kautz

    IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06K9/32 ,  G06T3/00 ,  G06T7/10

    CPC classification number: G06K9/6256 ,  G06K9/3233 ,  G06K9/6267 ,  G06N3/0454 ,  G06N3/08 ,  G06T3/0056 ,  G06T7/10

    Abstract: A style transfer neural network may be used to generate stylized synthetic images, where real images provide the style (e.g., seasons, weather, lighting) for transfer to synthetic images. The stylized synthetic images may then be used to train a recognition neural network. In turn, the trained neural network may be used to predict semantic labels for the real images, providing recognition data for the real images. Finally, the real training dataset (real images and predicted recognition data) and the synthetic training dataset are used by the style transfer neural network to generate stylized synthetic images. The training of the neural network, prediction of recognition data for the real images, and stylizing of the synthetic images may be repeated for a number of iterations. The stylization operation more closely aligns a covariate of the synthetic images to the covariate of the real images, improving accuracy of the recognition neural network.

    CREATING AN IMAGE UTILIZING A MAP REPRESENTING DIFFERENT CLASSES OF PIXELS
    96.
    发明申请
    CREATING AN IMAGE UTILIZING A MAP REPRESENTING DIFFERENT CLASSES OF PIXELS 审中-公开

    公开(公告)号：US20190147296A1

    公开(公告)日：2019-05-16

    申请号：US16188920

    申请日：2018-11-13

    Applicant: NVIDIA Corporation

    Inventor： Ting-Chun Wang ,  Ming-Yu Liu ,  Bryan Christopher Catanzaro ,  Jan Kautz ,  Andrew J. Tao

    IPC: G06K9/62 ,  G06K9/68 ,  G06K9/72

    Abstract: A method, computer readable medium, and system are disclosed for creating an image utilizing a map representing different classes of specific pixels within a scene. One or more computing systems use the map to create a preliminary image. This preliminary image is then compared to an original image that was used to create the map. A determination is made whether the preliminary image matches the original image, and results of the determination are used to adjust the computing systems that created the preliminary image, which improves a performance of such computing systems. The adjusted computing systems are then used to create images based on different input maps representing various object classes of specific pixels within a scene.

    Learning-Based Camera Pose Estimation From Images of an Environment
    97.
    发明申请
    Learning-Based Camera Pose Estimation From Images of an Environment 审中-公开

    公开(公告)号：US20190108651A1

    公开(公告)日：2019-04-11

    申请号：US16137064

    申请日：2018-09-20

    Applicant: NVIDIA Corporation

    Inventor： Jinwei Gu ,  Samarth Manoj Brahmbhatt ,  Kihwan Kim ,  Jan Kautz

    IPC: G06T7/80 ,  G06T7/00

    Abstract: A deep neural network (DNN) system learns a map representation for estimating a camera position and orientation (pose). The DNN is trained to learn a map representation corresponding to the environment, defining positions and attributes of structures, trees, walls, vehicles, walls, etc. The DNN system learns a map representation that is versatile and performs well for many different environments (indoor, outdoor, natural, synthetic, etc.). The DNN system receives images of an environment captured by a camera (observations) and outputs an estimated camera pose within the environment. The estimated camera pose is used to perform camera localization, i.e., recover the three-dimensional (3D) position and orientation of a moving camera, which is a fundamental task in computer vision with a wide variety of applications in robot navigation, car localization for autonomous driving, device localization for mobile navigation, and augmented/virtual reality.

    SYNTHETIC BRACKETING FOR EXPOSURE CORRECTION
    99.
    发明申请
    SYNTHETIC BRACKETING FOR EXPOSURE CORRECTION 有权

    公开(公告)号：US20250069191A1

    公开(公告)日：2025-02-27

    申请号：US18452634

    申请日：2023-08-21

    Applicant: NVIDIA Corporation

    Inventor： Iuri Frosio ,  Mayoore Selvarasa Jaiswal ,  Jan Kautz ,  Jianyuan Min

    IPC: G06T5/50 ,  H04N23/743

    Abstract: Systems and methods are disclosed related to synthetic bracketing for exposure correction. A deep learning based method and system produces a set of differently exposed images from a single input image. The images in the set may be combined to produce an output image with improved global and local exposure compared with the input image. An image encoder applies learned parameters to each input image to generate a set of image features including local exposure estimates for each of two or more regions of the input image and a low resolution latent representation of the input image. A decoder receives the local exposure estimates, the latent representation, and target enhancements that are processed to generate synthesized transformations. When applied to the input image, the synthesized transformations produce the set of transformed images. Each transformed image is a version of the input image synthesized to correspond to a respective target enhancement.

    VARIATIONAL INFERENCING BY A DIFFUSION MODEL
    100.
    发明申请
    VARIATIONAL INFERENCING BY A DIFFUSION MODEL 有权

    公开(公告)号：US20250045892A1

    公开(公告)日：2025-02-06

    申请号：US18593742

    申请日：2024-03-01

    Applicant: NVIDIA Corporation

    Inventor： Morteza Mardani ,  Jiaming Song ,  Jan Kautz ,  Arash Vahdat

    IPC: G06T7/00 ,  G06T3/40 ,  G06T5/70 ,  G06T5/73 ,  G06T5/77

    Abstract: Diffusion models are machine learning algorithms that are uniquely trained to generate high-quality data from an input lower-quality data. For example, they can be trained in the image domain, for example, to perform specific image restoration tasks, such as inpainting (e.g. completing an incomplete image), deblurring (e.g. removing blurring from an image), and super-resolution (e.g. increasing a resolution of an image), or they can be trained to perform image rendering tasks, including 2D-to-3D image generation tasks. However, current approaches to training diffusion models only allow the models to be optimized for a specific task such that they will not achieve high-quality results when used for other tasks. The present disclosure provides a diffusion model that uses variational inferencing to approximate a distribution of data, which allows the diffusion model to universally solve different tasks without having to be re-trained specifically for each task.

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