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    Fast multi-scale point cloud registration with a hierarchical gaussian mixture
    121.
    发明授权
    Fast multi-scale point cloud registration with a hierarchical gaussian mixture 有权

    公开(公告)号：US10826786B2

    公开(公告)日：2020-11-03

    申请号：US16351312

    申请日：2019-03-12

    Applicant: NVIDIA Corporation

    Inventor： Benjamin David Eckart ,  Kihwan Kim ,  Jan Kautz

    IPC: H04L12/24 ,  G06F17/18 ,  G06F16/22 ,  H04L29/08

    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.

    DEEP-LEARNING METHOD FOR SEPARATING REFLECTION AND TRANSMISSION IMAGES VISIBLE AT A SEMI-REFLECTIVE SURFACE IN A COMPUTER IMAGE OF A REAL-WORLD SCENE
    122.
    发明申请
    DEEP-LEARNING METHOD FOR SEPARATING REFLECTION AND TRANSMISSION IMAGES VISIBLE AT A SEMI-REFLECTIVE SURFACE IN A COMPUTER IMAGE OF A REAL-WORLD SCENE 审中-公开

    公开(公告)号：US20200342263A1

    公开(公告)日：2020-10-29

    申请号：US16924005

    申请日：2020-07-08

    Applicant: NVIDIA Corporation

    Inventor： Orazio Gallo ,  Jinwei Gu ,  Jan Kautz ,  Patrick Wieschollek

    IPC: G06K9/62

    Abstract: When a computer image is generated from a real-world scene having a semi-reflective surface (e.g. window), the computer image will create, at the semi-reflective surface from the viewpoint of the camera, both a reflection of a scene in front of the semi-reflective surface and a transmission of a scene located behind the semi-reflective surface. Similar to a person viewing the real-world scene from different locations, angles, etc., the reflection and transmission may change, and also move relative to each other, as the viewpoint of the camera changes. Unfortunately, the dynamic nature of the reflection and transmission negatively impacts the performance of many computer applications, but performance can generally be improved if the reflection and transmission are separated. The present disclosure uses deep learning to separate reflection and transmission at a semi-reflective surface of a computer image generated from a real-world scene.

    TRAINING A NEURAL NETWORK TO PREDICT SUPERPIXELS USING SEGMENTATION-AWARE AFFINITY LOSS
    123.
    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
    124.
    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.

    FEW-SHOT VIEWPOINT ESTIMATION
    125.
    发明申请
    FEW-SHOT VIEWPOINT ESTIMATION 审中-公开

    公开(公告)号：US20200252600A1

    公开(公告)日：2020-08-06

    申请号：US16780738

    申请日：2020-02-03

    Applicant: NVIDIA Corporation

    Inventor： Hung-Yu Tseng ,  Shalini De Mello ,  Jonathan Tremblay ,  Sifei Liu ,  Jan Kautz ,  Stanley Thomas Birchfield

    IPC: H04N13/282 ,  H04N13/268 ,  G06N3/08 ,  G06K9/62

    Abstract: When an image is projected from 3D, the viewpoint of objects in the image, relative to the camera, must be determined. Since the image itself will not have sufficient information to determine the viewpoint of the various objects in the image, techniques to estimate the viewpoint must be employed. To date, neural networks have been used to infer such viewpoint estimates on an object category basis, but must first be trained with numerous examples that have been manually created. The present disclosure provides a neural network that is trained to learn, from just a few example images, a unique viewpoint estimation network capable of inferring viewpoint estimations for a new object category.

    GUIDED HALLUCINATION FOR MISSING IMAGE CONTENT USING A NEURAL NETWORK
    126.
    发明申请
    GUIDED HALLUCINATION FOR MISSING IMAGE CONTENT USING A NEURAL NETWORK 审中-公开

    公开(公告)号：US20190355103A1

    公开(公告)日：2019-11-21

    申请号：US16353195

    申请日：2019-03-14

    Applicant: NVIDIA Corporation

    Inventor： Seung-Hwan Baek ,  Kihwan Kim ,  Jinwei Gu ,  Orazio Gallo ,  Alejandro Jose Troccoli ,  Ming-Yu Liu ,  Jan Kautz

    IPC: G06T5/00 ,  G06K9/72 ,  G06T5/50 ,  G06T3/40 ,  G06K9/62

    Abstract: Missing image content is generated using a neural network. In an embodiment, a high resolution image and associated high resolution semantic label map are generated from a low resolution image and associated low resolution semantic label map. The input image/map pair (low resolution image and associated low resolution semantic label map) lacks detail and is therefore missing content. Rather than simply enhancing the input image/map pair, data missing in the input image/map pair is improvised or hallucinated by a neural network, creating plausible content while maintaining spatio-temporal consistency. Missing content is hallucinated to generate a detailed zoomed in portion of an image. Missing content is hallucinated to generate different variations of an image, such as different seasons or weather conditions for a driving video.

    Photorealistic Image Stylization Using a Neural Network Model
    127.
    发明申请
    Photorealistic Image Stylization Using a Neural Network Model 审中-公开

    公开(公告)号：US20190244329A1

    公开(公告)日：2019-08-08

    申请号：US16246375

    申请日：2019-01-11

    Applicant: NVIDIA Corporation

    Inventor： Yijun Li ,  Ming-Yu Liu ,  Ming-Hsuan Yang ,  Jan Kautz

    IPC: G06T5/00 ,  G06T7/11 ,  G06N3/08

    CPC classification number: G06T5/002 ,  G06N3/088 ,  G06T7/11

    Abstract: Photorealistic image stylization concerns transferring style of a reference photo to a content photo with the constraint that the stylized photo should remain photorealistic. Examples of styles include seasons (summer, winter, etc.), weather (sunny, rainy, foggy, etc.), lighting (daytime, nighttime, etc.). A photorealistic image stylization process includes a stylization step and a smoothing step. The stylization step transfers the style of the reference photo to the content photo. A photo style transfer neural network model receives a photorealistic content image and a photorealistic style image and generates an intermediate stylized photorealistic image that includes the content of the content image modified according to the style image. A smoothing function receives the intermediate stylized photorealistic image and pixel similarity data and generates the stylized photorealistic image, ensuring spatially consistent stylizations.

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