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Scrap-SAM-CLIP: Assembling Foundation Models for Typical Shape Recognition in Scrap Classification and Rating.

Sensors (Basel, Switzerland)·2026

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Feasibility Study of Scrap Grading Systems Based on Three-Dimensional Vision Technology.

Guangda Bao¹, Wenzhi Xia¹, Yun Zhou¹

¹School of Metallurgical Engineering, Anhui University of Technology, Ma'anshan 243032, China.

Sensors (Basel, Switzerland)

|March 28, 2026

Summary

This study introduces 3D vision for automated scrap grading, achieving 1 mm accuracy. A novel workflow using 3D reconstruction and Euclidean clustering significantly improves grading reliability over 2D methods.

Keywords:

3D vision technology multi-view reconstruction point cloud segmentation scrap grading

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Area of Science:

Engineering
Computer Vision
Materials Science

Background:

Traditional manual scrap sorting is inefficient and prone to errors.
Automated grading systems are needed to improve accuracy and fairness.
3D vision technology offers potential for enhanced scrap characterization.

Purpose of the Study:

To investigate the application of 3D vision technology for automated scrap grading.
To develop and evaluate an automated processing workflow for scrap grading using 3D data.
To compare the performance of different point cloud segmentation methods for scrap analysis.

Main Methods:

Multi-view 3D reconstruction algorithm for generating accurate scrap models.
Development of an automated pipeline integrating 3D reconstruction and point cloud segmentation.
Comparison of Euclidean clustering, Kmeans, DBSCAN, and Region Grow for point cloud segmentation.
Thickness measurement using 3D data for scrap characterization.

Main Results:

Multi-view 3D reconstruction achieved accuracy within 1 mm in synthetic and real scrap scenes.
Euclidean-clustering-based segmentation yielded the best trade-off, with an mIoU score of 99.35%.
Thickness measurement error was less than 0.5 mm, demonstrating high precision.
The proposed 3D vision workflow showed improved robustness and reliability compared to 2D image-based methods.

Conclusions:

3D vision technology is suitable for accurate scrap grading, meeting precision requirements.
The developed automated workflow enhances the reliability and efficiency of scrap sorting.
3D vision provides a robust foundation for future advancements in automated scrap grading systems.