Abstract Emerging vision-based frameworks have demonstrated the great potential to robustly perform volumetric measurements on point cloud models, which has several applications for site material management (e.g., during earthworks). However, prevalent vision-based frameworks to date involve human interventions to manually trim objects of interest from point cloud models, which would be time-consuming and labor-intensive. In addition, point cloud models for volumetric measurements are often incomplete and noisy. To address such challenges, we automatically detect and segment target objects in point cloud models via a deep learning-based approach and then map the semantic values onto point cloud models for 3D semantic segmentation. Once target objects are segmented, the associated volumes are quantified through the proposed vision-based computational process. For evaluation, case studies were performed on material piles in the real-world. The proposed method has the potential to enhance vision-based volumetric measurements, which supports systematic decision-making for material management in jobsites.

中文翻译：

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通过基于深度学习的点云分割进行基于视觉的体积测量，用于工地材料管理

摘要 新兴的基于视觉的框架已经证明了在点云模型上稳健地执行体积测量的巨大潜力，点云模型具有多种现场材料管理应用（例如，在土方工程期间）。然而，迄今为止流行的基于视觉的框架涉及人工干预以从点云模型中手动修剪感兴趣的对象，这将是耗时且劳动密集型的。此外，用于体积测量的点云模型通常不完整且嘈杂。为了应对这些挑战，我们通过基于深度学习的方法自动检测和分割点云模型中的目标对象，然后将语义值映射到点云模型上进行 3D 语义分割。一旦目标对象被分割，相关的体积通过提出的基于视觉的计算过程进行量化。为了进行评估，我们对现实世界中的材料桩进行了案例研究。所提出的方法有可能增强基于视觉的体积测量，从而支持工地材料管理的系统决策。