MatchMesh: Knowledge-based 3D Point Cloud Meshing Using Divide-and-conquer Deformation

Ying Tang, Shengtao Sun, Ben Wu

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    The reconstruction of surface mesh from point cloud is compute-intensive but also very important step in the remanufacturing and personalization industries. With more 3D scanners providing lower cost and higher resolution, further detailed point clouds can be gathered without so much effort as before. In manufacturing, there are databases which contain the origin 3D design models of the products. How to utilize the design model data for swift production of related products remains a problem for remanufacturing and customization. In order to develop a knowledge-based way of handling this problem, editing or deforming an existing mesh to match the target is an effective way of easing the workload. In this paper, we introduce a divide-and -conquer process which segments the depth scan data and then find the best match in the database as its source of deformation. The segmentation is performed on 3D point level using global features extracted by 3D CNN. After that we find best match to our knowledge with the same features to acquire a fast meshing of the target object by deforming the existing parts from the match. The deformation of parts are being done sequentially. For further performance improvement, we present a deformation training method employing transfer learning on segment editing process.

    Original languageEnglish (US)
    Title of host publication2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781728168531
    DOIs
    StatePublished - Oct 30 2020
    Event2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020 - Nanjing, China
    Duration: Oct 30 2020Nov 2 2020

    Publication series

    Name2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

    Conference

    Conference2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
    Country/TerritoryChina
    CityNanjing
    Period10/30/2011/2/20

    All Science Journal Classification (ASJC) codes

    • Artificial Intelligence
    • Computer Networks and Communications
    • Statistics, Probability and Uncertainty
    • Control and Optimization
    • Sensory Systems

    Fingerprint

    Dive into the research topics of 'MatchMesh: Knowledge-based 3D Point Cloud Meshing Using Divide-and-conquer Deformation'. Together they form a unique fingerprint.

    Cite this