IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v6y2023i1p16-278d1059245.html
   My bibliography  Save this article

Point Cloud Registration via Heuristic Reward Reinforcement Learning

Author

Listed:
  • Bingren Chen

    (Data Mining Laboratory, Dalian University of Technology, Dalian 116000, China)

Abstract

This paper proposes a heuristic reward reinforcement learning framework for point cloud registration. As an essential step of many 3D computer vision tasks such as object recognition and 3D reconstruction, point cloud registration has been well studied in the existing literature. This paper contributes to the literature by addressing the limitations of embedding and reward functions in existing methods. An improved state-embedding module and a stochastic reward function are proposed. While the embedding module enriches the captured characteristics of states, the newly designed reward function follows a time-dependent searching strategy, which allows aggressive attempts at the beginning and tends to be conservative in the end. We assess our method based on two public datasets (ModelNet40 and ScanObjectNN) and real-world data. The results confirm the strength of the new method in reducing errors in object rotation and translation, leading to more precise point cloud registration.

Suggested Citation

  • Bingren Chen, 2023. "Point Cloud Registration via Heuristic Reward Reinforcement Learning," Stats, MDPI, vol. 6(1), pages 1-11, February.
    • Handle: RePEc:gam:jstats:v:6:y:2023:i:1:p:16-278:d:1059245
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/6/1/16/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-905X/6/1/16/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tian Zhu & Merry H. Ma, 2022. "Deriving the Optimal Strategy for the Two Dice Pig Game via Reinforcement Learning," Stats, MDPI, vol. 5(3), pages 1-14, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jstats:v:6:y:2023:i:1:p:16-278:d:1059245. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.