Advanced Photonics Nexus, Volume. , Issue , ()

Object Pose and Surface Material Recognition Using a Single-ToF-Camera [Early Posting]

Yang Dongzhao, An Dong, Xu Tianxu, Zhang Yiwen, Wang Qiang, Pan Z, Yue Yang
Author Affiliations
  • Xi''''an Jiaotong University School of Electronic and Information Engineering
  • Nankai University Institute of Modern Optics
  • Zhengzhou University
  • China
  • University of Louisiana at Lafayette
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    In this work, a novel approach is proposed for recognizing the pose and surface material of diverse objects, leveraging diffuse reflection principles and data fusion. Through theoretical analysis and the derivation of factors influencing diffuse reflection on objects, the method concentrates on and exploits surface information. To validate the feasibility of our theoretical research, the depth and active infrared intensity (D-AI) data obtained from a single Time-of-Flight (ToF) camera are initially combined. Subsequently, these data undergo processing using feature extraction and lightweight machine learning techniques. Additionally, an optimization method is introduced to enhance the fitting of intensity. The experimental results not only visually showcase the effectiveness of our proposed method in accurately detecting the positions and surface materials of targets with varying sizes and spatial locations, but also reveal that the vast majority of the sample data can achieve recognition accuracy of 94.8% or higher.

    Paper Information

    Manuscript Accepted: Mar. 29, 2024

    Posted: May. 15, 2024

    DOI: APN