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    GAZE POINT ESTIMATION METHOD, DEVICE, AND ELECTRONIC DEVICE
    1.
    发明申请
    GAZE POINT ESTIMATION METHOD, DEVICE, AND ELECTRONIC DEVICE 有权

    公开(公告)号：US20220179485A1

    公开(公告)日：2022-06-09

    申请号：US17545932

    申请日：2021-12-08

    Applicant: BEIHANG UNIVERSITY

    Inventor： FENG LU ,  YIWEI BAO ,  QINPING ZHAO

    IPC: G06F3/01 ,  G06K9/62 ,  G06V40/18 ,  G06V10/80 ,  G06V40/16 ,  G06N3/02

    Abstract: The present application provides a gaze point estimation method, device, and an electronic device. The method includes: acquiring user image data; acquiring a facial feature vector according to a preset first convolutional neural network and the facial image; acquiring a position feature vector according to a preset first fully connected network and the position data; acquiring a binocular fusion feature vector according to a preset eye feature fusion network, the left-eye image and the right-eye image; and acquiring position information about a gaze point of a user according to a preset second fully connected network, the facial feature vector, the position feature vector, and the binocular fusion feature vector. In this technical solution, relation between eye images and face images is utilized to achieve accurate gaze point estimation.

    METHOD AND DEVICE FOR THREE-DIMENSIONAL FEATURE-EMBEDDED IMAGE OBJECT COMPONENT-LEVEL SEMANTIC SEGMENTATION
    2.
    发明申请
    METHOD AND DEVICE FOR THREE-DIMENSIONAL FEATURE-EMBEDDED IMAGE OBJECT COMPONENT-LEVEL SEMANTIC SEGMENTATION 审中-公开

    公开(公告)号：US20190080455A1

    公开(公告)日：2019-03-14

    申请号：US15871878

    申请日：2018-01-15

    Applicant: BEIHANG UNIVERSITY

    Inventor： XIAOWU CHEN ,  JIA LI ,  YAFEI SONG ,  YIFAN ZHAO ,  QINPING ZHAO

    IPC: G06T7/10 ,  G06N3/08

    Abstract: Embodiments of the present invention provide a method and a device for three-dimensional feature-embedded image object component-level semantic segmentation, the method includes: acquiring three-dimensional feature information of a target two-dimensional image; performing a component-level semantic segmentation on the target two-dimensional image according to the three-dimensional feature information of the target two-dimensional image and two-dimensional feature information of the target two-dimensional image. In the technical solution of the present application, not only the two-dimensional feature information of the image but also the three-dimensional feature information of the image are taken into consideration when performing the component-level semantic segmentation on the image, thereby improving the accuracy of the image component-level semantic segmentation.

    METHOD FOR REAL-TIME CUTTING OF DIGITAL ORGAN BASED ON METABALL MODEL AND HYBRID DRIVING METHOD
    4.
    发明申请
    METHOD FOR REAL-TIME CUTTING OF DIGITAL ORGAN BASED ON METABALL MODEL AND HYBRID DRIVING METHOD 审中-公开

    公开(公告)号：US20180204378A1

    公开(公告)日：2018-07-19

    申请号：US15835408

    申请日：2017-12-07

    Applicant: BEIHANG UNIVERSITY

    Inventor： JUN PAN ,  SHIZENG YAN ,  QINPING ZHAO

    IPC: G06T17/20 ,  G06T17/30 ,  G06T19/20 ,  A61B34/10

    Abstract: Disclosed is a method for real-time cutting of digital organ based on a metaball model and a hybrid driving method, including a cutting procedure for driving a model using a position-based dynamics and a meshless method, a cutting mode which begins from a metaball driven by the position-based dynamics, proceeds to a point set driven by the meshless method and then create a new metaball. The method includes: a preprocessing procedure which performs an initialization operation while reading a model file; a deforming procedure which drives a model using a method based on the position-based dynamics; a cutting procedure which drives the model using the hybrid driving method and performs cutting using said cutting mode; and a rendering procedure which renders the model during the second and third procedures.

    METHOD AND APPARATUS FOR EYE MOVEMENT SYNTHESIS
    5.
    发明申请
    METHOD AND APPARATUS FOR EYE MOVEMENT SYNTHESIS 审中-公开

    公开(公告)号：US20200349750A1

    公开(公告)日：2020-11-05

    申请号：US16725634

    申请日：2019-12-23

    Applicant: BEIHANG UNIVERSITY

    Inventor： FENG LU ,  QINPING ZHAO

    IPC: G06T13/40 ,  G06T13/20 ,  G06F3/01 ,  G06F3/16

    Abstract: Embodiments of the present disclosure provide a method and an apparatus for eye movement synthesis, the method including: obtaining eye movement feature data and speech feature data, wherein the eye movement feature data reflects an eye movement behavior, and the speech feature data reflects a voice feature; obtaining a driving model according to the eye movement feature data and the speech feature data, wherein the driving model is configured to indicate an association between the eye movement feature data and the speech feature data; synthesizing an eye movement of a virtual human according to speech input data and the driving model and controlling the virtual human to exhibit the synthesized eye movement. The embodiment makes the virtual human to exhibit an eye movement corresponding to the voice data according to the eye movement feature data and the speech feature data, thereby improving the authenticity in the interaction.

    FINE-GRAINED IMAGE RECOGNITION METHOD AND APPARATUS USING GRAPH STRUCTURE REPRESENTED HIGH-ORDER RELATION DISCOVERY
    9.
    发明申请
    FINE-GRAINED IMAGE RECOGNITION METHOD AND APPARATUS USING GRAPH STRUCTURE REPRESENTED HIGH-ORDER RELATION DISCOVERY 有权

    公开(公告)号：US20220382553A1

    公开(公告)日：2022-12-01

    申请号：US17546993

    申请日：2021-12-09

    Applicant: BEIHANG UNIVERSITY

    Inventor： JIA LI ,  YIFAN ZHAO ,  DINGFENG SHI ,  QINPING ZHAO

    IPC: G06F9/38 ,  G06F9/30 ,  G06N3/02

    Abstract: Embodiments of the present disclosure provides a fine-grained image recognition method and apparatus using graph structure represented high-order relation discovery, wherein the method includes: inputting an image to be classified into a convolutional neural network feature extractor with multiple stages, extracting two layers of network feature graphs in the last stage, constructing a hybrid high-order attention module according to the network feature graphs, and forming a high-order feature vector pool according to the hybrid high-order attention module, using each vector in the vector pool as a node, and utilizing semantic similarity among high-order features to form representative vector nodes in groups, and performing global pooling on the representative vector nodes to obtain classification vectors, and obtaining a fine-grained classification result through a fully connected layer and a classifier based on the classification vectors.

    METHOD AND APPARATUS FOR PARSING AND PROCESSING THREE-DIMENSIONAL CAD MODEL
    10.
    发明申请
    METHOD AND APPARATUS FOR PARSING AND PROCESSING THREE-DIMENSIONAL CAD MODEL 审中-公开

    公开(公告)号：US20190087964A1

    公开(公告)日：2019-03-21

    申请号：US16132345

    申请日：2018-09-14

    Applicant: BEIHANG UNIVERSITY

    Inventor： XIAOWU CHEN ,  XIAOGANG WANG ,  BIN ZHOU ,  HAIYUE FANG ,  QINPING ZHAO

    IPC: G06T7/143 ,  G06N7/00 ,  G06N3/08 ,  G06T15/08 ,  G06K9/72 ,  G06K9/62 ,  G06T17/00

    Abstract: The present application provides a method and an apparatus for parsing and processing a three-dimensional CAD model, where the method includes: determining three kinds of adjacency relation information for each component in the three-dimensional model; performing aggregation processing on all components of the three-dimensional model, and generating three part hypothesis sets for the three-dimensional model; performing voxelization expression processing on each part hypothesis in each part hypothesis set, and generating voxelization information for each part hypothesis; inputting voxelization information of all part hypotheses in each part hypothesis set into an identification model to obtain a confidence score and a semantic category probability distribution for each part hypothesis; and constructing, according to the confidence score and the semantic category probability distribution of each part hypothesis in the part hypothesis sets, a high-order conditional random field model, and obtaining a semantic category analysis result for each component in the three-dimensional model.

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