Evaluation of point cloud filtering techniques in object dimensioning via vision-based geometric measurement systems
Artykuł w czasopiśmie
MNiSW
100
Lista 2024
| Status: | |
| Autorzy: | Kuziora Karol, Sereda Robert, Badurowicz Marcin, Smoliński Konrad |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 12 |
| Wolumen/Tom: | 19 |
| Strony: | 307 - 322 |
| Impact Factor: | 1,3 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 1 listopada 2025 |
| Abstrakty: | angielski |
| In this study, we evaluate the influence of different cloud point filtering algorithms on the process of accurately di- mensioning objects. This is critical in vision-based measurement systems, particularly for logistics and packaging applications. We assess three smoothing algorithms: bilateral filtering, statistical outlier removal, shadow filtering algorithm alongside baseline unfiltered data. We extract object dimensions by fitting a convex hull applied to the processed point cloud, and evaluate across different positions, parcel types, and edge lengths. We employ various statistical metrics to evaluate algorithm performance. Our research utilizes point clouds of cardboard boxes for evaluation, collected with the ToF Kinect v2 depth camera. Study includes both cuboidal objects and distortion- simulated shapes. We assessed a dataset of 639-point cloud samples. The data was collected under controlled lighting with top-down camera orientation and processed using the PCL library. Our findings show that shadow filtering consistently and significantly outperforms the other methods on standard cuboid geometries. However, in the presence of shape distortions, it occasionally introduces large-magnitude outliers, reflecting overly aggressive filtering behaviour. Additionally, we observe scale-dependent error pattern across all object types, with dimen- sional accuracy decreasing as object size increases |
