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    SYSTEMS AND METHODS FOR MULTI-CAMERA PLACEMENT
    1.
    发明申请
    SYSTEMS AND METHODS FOR MULTI-CAMERA PLACEMENT 有权

    公开(公告)号：US20250047983A1

    公开(公告)日：2025-02-06

    申请号：US18740457

    申请日：2024-06-11

    Applicant: PACKSIZE, LLC

    Inventor： Carlo Dal Mutto ,  Giulio Marin

    IPC: H04N23/698 ,  G06T7/73 ,  G06T7/80

    Abstract: A method for calibrating a camera group includes: controlling a first and second cameras to capture first and second images of a first scene, the first camera having a first field of view FOV), the second camera having a second FOV overlapping the first FOV in a first overlap region; the first scene including a calibration target in the first overlap region; computing a first relative pose based on the first and second images; controlling the second camera and a third camera to capture third and fourth images of a second scene, the third camera having a third FOV overlapping the second FOV in a second overlap region, the second scene including a calibration target in the second overlap region; computing a second relative pose based on the third and fourth images; and computing a third relative pose based on the first and second relative poses.

    SYSTEMS AND METHODS FOR KEYPOINT DETECTION WITH CONVOLUTIONAL NEURAL NETWORKS
    4.
    发明申请
    SYSTEMS AND METHODS FOR KEYPOINT DETECTION WITH CONVOLUTIONAL NEURAL NETWORKS 有权

    公开(公告)号：US20230054821A1

    公开(公告)日：2023-02-23

    申请号：US17831818

    申请日：2022-06-03

    Applicant: PACKSIZE LLC

    Inventor： Paolo Di Febbo ,  Carlo Dal Mutto ,  Kinh Tieu

    IPC: G06K9/62 ,  G06N3/08 ,  G06T7/00 ,  G06T7/246 ,  G06N3/04 ,  G06V10/44 ,  G06V10/46 ,  G06V10/75

    Abstract: A keypoint detection system includes: a camera system including at least one camera; and a processor and memory, the processor and memory being configured to: receive an image captured by the camera system; compute a plurality of keypoints in the image using a convolutional neural network including: a first layer implementing a first convolutional kernel; a second layer implementing a second convolutional kernel; an output layer; and a plurality of connections between the first layer and the second layer and between the second layer and the output layer, each of the connections having a corresponding weight stored in the memory; and output the plurality of keypoints of the image computed by the convolutional neural network.

    Systems and methods for object dimensioning based on partial visual information
    5.
    发明授权
    Systems and methods for object dimensioning based on partial visual information 有权

    公开(公告)号：US11481885B2

    公开(公告)日：2022-10-25

    申请号：US16724029

    申请日：2019-12-20

    Applicant: PACKSIZE LLC

    Inventor： Carlo Dal Mutto ,  Kinh Tieu ,  Francesco Peruch ,  Jason Trachewsky ,  Tony Zuccarino ,  Chase Garber

    IPC: G06T7/00 ,  G06T7/10 ,  H04N13/20 ,  G06N3/08 ,  G06T3/40 ,  G06T17/00

    Abstract: A method for estimating tightly enclosing bounding boxes by a computing system includes: controlling a scanning system including one or more depth cameras to capture visual information of the scene including one or more objects; detecting the one or more objects of the scene based on the visual information; singulating each the one or more objects from the frame of the scene to generate one or more 3D models corresponding to the one or more objects, the one or more 3D models including a partial 3D model of a corresponding one of the one or more objects; extrapolating a more complete 3D model of the corresponding one of the one or more objects based on the partial 3D model; and estimating a tightly enclosing bounding box of the corresponding one of the one or more objects based on the more complete 3D model.

    SYSTEMS AND METHODS FOR KEYPOINT DETECTION WITH CONVOLUTIONAL NEURAL NETWORKS
    8.
    发明申请
    SYSTEMS AND METHODS FOR KEYPOINT DETECTION WITH CONVOLUTIONAL NEURAL NETWORKS 有权

    公开(公告)号：US20240381645A1

    公开(公告)日：2024-11-14

    申请号：US18780324

    申请日：2024-07-22

    Applicant: PACKSIZE LLC

    Inventor： Paolo Di Febbo ,  Carlo Dal Mutto ,  Kinh Tieu

    IPC: H10B43/27 ,  H10B41/10 ,  H10B41/27 ,  H10B43/10

    Abstract: A keypoint detection system includes: a camera system including at least one camera; and a processor and memory, the processor and memory being configured to: receive an image captured by the camera system; compute a plurality of keypoints in the image using a convolutional neural network including: a first layer implementing a first convolutional kernel; a second layer implementing a second convolutional kernel; an output layer; and a plurality of connections between the first layer and the second layer and between the second layer and the output layer, each of the connections having a corresponding weight stored in the memory; and output the plurality of keypoints of the image computed by the convolutional neural network.

    Systems and methods for joint learning of complex visual inspection tasks using computer vision
    9.
    发明授权
    Systems and methods for joint learning of complex visual inspection tasks using computer vision 有权

    公开(公告)号：US11508050B2

    公开(公告)日：2022-11-22

    申请号：US16721501

    申请日：2019-12-19

    Applicant: PACKSIZE LLC

    Inventor： Carlo Dal Mutto ,  Francesco Peruch ,  Alexander Ou ,  Robert Hayes

    IPC: G06T7/00 ,  G06T7/50 ,  G06T7/90 ,  G06K9/62 ,  G06N3/04 ,  G06V30/148 ,  G06V30/10

    Abstract: A method for performing automatic visual inspection includes: capturing visual information of an object using a scanning system including a plurality of cameras; extracting, by a computing system including a processor and memory, one or more feature maps from the visual information using one or more feature extractors; classifying, by the computing system, the object by supplying the one or more feature maps to a complex classifier to compute a classification of the object, the complex classifier including: a plurality of simple classifiers, each simple classifier of the plurality of simple classifiers being configured to compute outputs representing a characteristic of the object; and one or more logical operators configured to combine the outputs of the simple classifiers to compute the classification of the object; and outputting, by the computing system, the classification of the object as a result of the automatic visual inspection.

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