Deep Depth Fusion for Black, Transparent, Reflective and Texture-Less Objects

Chun Yu Chai, Yu Po Wu, Shiao Li Tsao

    研究成果: Conference contribution同行評審

    摘要

    Structured-light and stereo cameras, which are widely used to construct point clouds for robotic applications, have different limitations on estimating depth values. Structured-light cameras fail in black, transparent, and reflective objects, which influence the light path; stereo cameras fail in texture-less objects. In this work, we propose a depth fusion model that complements these two types of methods to generate high-quality point clouds for short-range robotic applications. The model first determines the fusion weights from the two input depth images and then refines the fused depth using color features. We construct a dataset containing the aforementioned challenging objects and report the performance of our proposed model. The results reveal that our method reduces the average L1 distance on depth prediction by 75% and 52% compared with the original depth output of the structured-light camera and the stereo model, respectively. A noticeable improvement on the Iterative Closest Point (ICP) algorithm can be achieved by using the refined depth images output from our method.

    原文English
    主出版物標題2020 IEEE International Conference on Robotics and Automation, ICRA 2020
    發行者Institute of Electrical and Electronics Engineers Inc.
    頁面6766-6772
    頁數7
    ISBN(電子)9781728173955
    DOIs
    出版狀態Published - 五月 2020
    事件2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France
    持續時間: 31 五月 202031 八月 2020

    出版系列

    名字Proceedings - IEEE International Conference on Robotics and Automation
    ISSN(列印)1050-4729

    Conference

    Conference2020 IEEE International Conference on Robotics and Automation, ICRA 2020
    國家/地區France
    城市Paris
    期間31/05/2031/08/20

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