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<p>Internet of Things (IoT) communication technology is widely used in industry. IoT LPWAN technology is used to build a binocular vision 3D reconstruction system for garden scenes to improve the problem of insufficient 3D image construction of garden scenes. By analyzing the imaging principle of binocular vision, the camera calibration method is optimized, and the binocular vision model is constructed. The feature processing and extraction of binocular vision are key to 3D scene construction, but traditional binocular vision systems have always faced difficulties in scene feature extraction, which affects the composition effect of the scene. Therefore, on the basis of the traditional SURF feature extraction algorithm, a SURF-B matching algorithm combining LDB feature description is proposed for extraction of image feature information. This performance experiment showed that in multi-view image feature matching, the feature matching errors of SIFT, SURF, and the proposed SURF-B algorithms were 125, 100, and 45, and the matching errors were 0.220, 0.115, and 0.036, respectively. At the same time, in the multi-algorithm matching accuracy test, the proposed SURF-B algorithm also had excellent matching accuracy and convergence performance. The research content has important reference significance for improving the composition effect of the garden scene and the layout effect of the garden landscape.</p>

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3D reconstruction method of landscape garden scene based on internet of things wireless communication

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月25日

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  • Hui Chi,
  • Danling Wang

摘要

Internet of Things (IoT) communication technology is widely used in industry. IoT LPWAN technology is used to build a binocular vision 3D reconstruction system for garden scenes to improve the problem of insufficient 3D image construction of garden scenes. By analyzing the imaging principle of binocular vision, the camera calibration method is optimized, and the binocular vision model is constructed. The feature processing and extraction of binocular vision are key to 3D scene construction, but traditional binocular vision systems have always faced difficulties in scene feature extraction, which affects the composition effect of the scene. Therefore, on the basis of the traditional SURF feature extraction algorithm, a SURF-B matching algorithm combining LDB feature description is proposed for extraction of image feature information. This performance experiment showed that in multi-view image feature matching, the feature matching errors of SIFT, SURF, and the proposed SURF-B algorithms were 125, 100, and 45, and the matching errors were 0.220, 0.115, and 0.036, respectively. At the same time, in the multi-algorithm matching accuracy test, the proposed SURF-B algorithm also had excellent matching accuracy and convergence performance. The research content has important reference significance for improving the composition effect of the garden scene and the layout effect of the garden landscape.