Invention Grant
- Patent Title: Automated semantic segmentation of non-euclidean 3D data sets using deep learning
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Application No.: US17415465Application Date: 2019-12-17
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Publication No.: US12141702B2Publication Date: 2024-11-12
- Inventor: Frank Theodorus Catharina Claessen , David Anssari Moin , Teo Cherici , Farhad Ghazvinian Zanjani
- Applicant: PROMATON HOLDING B.V.
- Applicant Address: NL Amsterdam
- Assignee: PROMATON HOLDING B.V.
- Current Assignee: PROMATON HOLDING B.V.
- Current Assignee Address: NL Amsterdam
- Agency: Westman, Champlin & Kochler, P.A.
- Agent Steven M. Koehler
- Priority: EP18213246 20181217
- International Application: PCT/EP2019/085819 WO 20191217
- International Announcement: WO2020/127398 WO 20200625
- Main IPC: G06N3/084
- IPC: G06N3/084 ; G06F18/21 ; G06F18/22 ; G06F18/2431 ; G06T7/00 ; G06T7/11 ; G06V10/26 ; G06V10/44 ; G06V10/764 ; G06V10/82 ; G06V20/64

Abstract:
A computer-implemented method for semantic segmentation of a point cloud comprises receiving a cloud having points representing a vector of an object, preferably part of a dento-maxillofacial structure having a dentition; determining subset(s) including a first number of points arranged around a selected point of the cloud and a second number of points arranged at spatial distances larger than a predetermined spatial distance of the first number of points, the first number of points representing fine feature(s) of the object around the selected point and the second number of points representing object global feature(s); providing each subset of points to a deep neural network, DNN, the DNN being trained to semantically segment points of each subset according to classes associated with the object; and, for each subset point, receiving a DNN output multi-element vector, wherein each element represents a probability that the point belongs to class(es) of the object.
Public/Granted literature
- US20220067943A1 AUTOMATED SEMANTIC SEGMENTATION OF NON-EUCLIDEAN 3D DATA SETS USING DEEP LEARNING Public/Granted day:2022-03-03
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