| dc.contributor.advisor | Giannakopoulos, Theodoros | |
| dc.contributor.advisor | Γιαννακόπουλος, Θεόδωρος | |
| dc.contributor.author | Vaggelis, Orestis | |
| dc.contributor.author | Βαγγέλης, Ορέστης | |
| dc.date.accessioned | 2025-10-21T14:13:48Z | |
| dc.date.available | 2025-10-21T14:13:48Z | |
| dc.date.issued | 2025-09 | |
| dc.identifier.uri | https://dione.lib.unipi.gr/xmlui/handle/unipi/18243 | |
| dc.format.extent | 74 | el |
| dc.language.iso | en | el |
| dc.publisher | Πανεπιστήμιο Πειραιώς | el |
| dc.rights | Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα | * |
| dc.title | 6D object pose estimation : literature review and model-free mask generation pipeline | el |
| dc.type | Master Thesis | el |
| dc.contributor.department | Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Ψηφιακών Συστημάτων | el |
| dc.description.abstractEN | This thesis presents a three-part investigation into 6D object pose estimation for novel objects. The first two parts consist of a comprehensive literature review and a unified evaluation of state-of-the-art methods on benchmark datasets. This analysis identifies a critical performance bottleneck for model-free approaches: the lack of robust and accurate initial object segmentation. Motivated by this finding, the third and principal contribution of this work is the development of DiPose, a novel pipeline focused specifically on generating high-quality segmentation masks for model-free pose estimation. DiPose models a novel object by first performing a Structure-from-Motion (SfM) reconstruction from a brief onboarding video. The resulting point cloud is then used to learn a high-fidelity implicit representation via Fast Dipole Sums (FDS). This implicit model acts as a virtual CAD model, enabling the generation of synthetic 2D views that drive a foundation model-based framework to produce precise segmentation masks for test images.The proposed pipeline is validated on the HOPE dataset, where it outperforms a strong model- free baseline by 8 % in average precision. | el |
| dc.corporate.name | National Center of Scientific Research "Demokritos" | el |
| dc.contributor.master | Τεχνητή Νοημοσύνη - Artificial Intelligence | el |
| dc.subject.keyword | 6D object pose estimation | el |
| dc.subject.keyword | Neural radiance fields | el |
| dc.subject.keyword | Instance segmentation | el |
| dc.date.defense | 2025-09 | |
