Vision-based volumetric measurements via deep learning-based point cloud segmentation for material management in jobsites

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.

Automated summary

The study demonstrates the potential for robust volumetric measurements on point cloud models using deep learning to automatically detect and segment target objects, mapping semantic values for 3D segmentation. Case studies on real-world material piles show promise in enhancing vision-based measurements and supporting decision-making for material management in jobsites.