3DOR 2025 - Short Papers

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/3607215

September 4 -5, 2025 | London, UK

Table of Contents
Short Papers
PhyDeformer: High-Quality Non-Rigid Garment Registration with Physics-Awareness
[full paper Image] [meta data Image]
Boyang Yu, Frederic Cordier, and Hyewon Seo
Coupling Self-Distillation with Test Time Augmentation for effective LiDAR-Based 3D Semantic Segmentation
[full paper Image] [meta data Image]
Dimitrios Antonarakos, Georgios Zamanakos, Ilias Papadeas, and Ioannis Pratikakis
SHREC'25 track: Retrieval and Segmentation of Multiple Relief Patterns
[full paper Image] [meta data Image]
Gabriele Paolini, Claudio Tortorici, and Stefano Berretti

BibTeX (3DOR 2025 - Short Papers)
@inproceedings{
10.2312:3dor.20252019,

booktitle = {
Eurographics Workshop on 3D Object Retrieval},

editor = {
Guerrero, Paul
 and 
Pratikakis, Ioannis
 and 
Veltkamp, Remco
},
title = {{
Eurographics Workshop on 3D Object Retrieval - Short Papers: Frontmatter}},

author = {
Guerrero, Paul
 and 
Pratikakis, Ioannis
 and 
Veltkamp, Remco
},
year = {
2025},

publisher = {
The Eurographics Association},


ISSN = {1997-0471},
ISBN = {978-3-03868-280-6},

DOI = {
10.2312/3dor.20252019}

}
@inproceedings{
10.2312:3dor.20251200,

booktitle = {
Eurographics Workshop on 3D Object Retrieval},

editor = {
Guerrero, Paul
 and 
Pratikakis, Ioannis
 and 
Veltkamp, Remco
},
title = {{
PhyDeformer: High-Quality Non-Rigid Garment Registration with Physics-Awareness}},

author = {
Yu, Boyang
 and 
Cordier, Frederic
 and 
Seo, Hyewon
},
year = {
2025},

publisher = {
The Eurographics Association},


ISSN = {1997-0471},
ISBN = {978-3-03868-280-6},

DOI = {
10.2312/3dor.20251200}

}
@inproceedings{
10.2312:3dor.20251201,

booktitle = {
Eurographics Workshop on 3D Object Retrieval},

editor = {
Guerrero, Paul
 and 
Pratikakis, Ioannis
 and 
Veltkamp, Remco
},
title = {{
Coupling Self-Distillation with Test Time Augmentation for effective LiDAR-Based 3D Semantic Segmentation}},

author = {
Antonarakos, Dimitrios
 and 
Zamanakos, Georgios
 and 
Papadeas, Ilias
 and 
Pratikakis, Ioannis
},
year = {
2025},

publisher = {
The Eurographics Association},


ISSN = {1997-0471},
ISBN = {978-3-03868-280-6},

DOI = {
10.2312/3dor.20251201}

}
@inproceedings{
10.2312:3dor.20251202,

booktitle = {
Eurographics Workshop on 3D Object Retrieval},

editor = {
Guerrero, Paul
 and 
Pratikakis, Ioannis
 and 
Veltkamp, Remco
},
title = {{
SHREC'25 track: Retrieval and Segmentation of Multiple Relief Patterns}},

author = {
Paolini, Gabriele
 and 
Tortorici, Claudio
 and 
Berretti, Stefano
},
year = {
2025},

publisher = {
The Eurographics Association},


ISSN = {1997-0471},
ISBN = {978-3-03868-280-6},

DOI = {
10.2312/3dor.20251202}

}

Browse

Recent Submissions

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Eurographics Workshop on 3D Object Retrieval - Short Papers: Frontmatter
    (The Eurographics Association, 2025) Guerrero, Paul; Pratikakis, Ioannis; Veltkamp, Remco; Guerrero, Paul; Pratikakis, Ioannis; Veltkamp, Remco
  • Thumbnail Image
    Item
    PhyDeformer: High-Quality Non-Rigid Garment Registration with Physics-Awareness
    (The Eurographics Association, 2025) Yu, Boyang; Cordier, Frederic; Seo, Hyewon; Guerrero, Paul; Pratikakis, Ioannis; Veltkamp, Remco
    Accurately registering 3D garment meshes to real-world image data is a fundamental yet challenging task in computer vision and graphics, with applications in virtual try-on systems, digital fashion, performance capture, and virtual content creation. This problem involves recovering detailed, non-rigid garment geometry from partial, noisy, and often ambiguous visual cues extracted from 2D or reconstructed 3D data. A key challenge lies in aligning garment templates with target shapes while preserving realistic fabric behavior and accommodating variations in body shape, garment fit, and pose. We present PhyDeformer, a new deformation method for high-quality garment mesh registration. It operates in two phases: In the first phase, a garment grading is performed to achieve a coarse 3D alignment between the mesh template and the target mesh, accounting for proportional scaling and fit (e.g. length, size). In the second phase, the graded mesh is refined to capture fine-grained geometric details of the 3D target through a localized optimization process, leveraging a Jacobian-based deformation framework. Both quantitative and qualitative evaluations on synthetic and real garment data demonstrate the effectiveness and robustness of our method in achieving accurate and visually plausible registrations. The code and base meshes generated and evaluated in this paper are available at https://github.com/MLMS-CG/PhyDeformer.
  • Thumbnail Image
    Item
    Coupling Self-Distillation with Test Time Augmentation for effective LiDAR-Based 3D Semantic Segmentation
    (The Eurographics Association, 2025) Antonarakos, Dimitrios; Zamanakos, Georgios; Papadeas, Ilias; Pratikakis, Ioannis; Guerrero, Paul; Pratikakis, Ioannis; Veltkamp, Remco
    Effective 3D perception is fundamental for spatial awareness and safe navigation in modern autonomous systems, with 3D semantic segmentation of LiDAR point clouds being a critical perception task. Recent progress in 2D vision highlights the potential of non-architectural training and inference strategies to further boost model performance. Inspired by consistency-based learning and self-distillation, this work employs such a training pipeline for robust 3D semantic segmentation in street scene understanding. Specifically, we incorporate a teacher-student knowledge self-distillation framework that integrates Test-Time Augmentation to enhance the quality of the soft labels generated by the teacher model during training and to improve inference performance. We present a comparative study on the effectiveness of the employed framework across both convolutional and attention-enhanced networks. Experimental results on the Street3D benchmark dataset demonstrate that the adopted training framework coupled with attention-enhanced networks compares favorably with the state-of-the-art for 3D semantic segmentation in the context of autonomous driving. Code is available at https://github.com/DUTH-VCG/Self_Distillation_with_TTA-main
  • Thumbnail Image
    Item
    SHREC'25 track: Retrieval and Segmentation of Multiple Relief Patterns
    (The Eurographics Association, 2025) Paolini, Gabriele; Tortorici, Claudio; Berretti, Stefano; Guerrero, Paul; Pratikakis, Ioannis; Veltkamp, Remco
    This SHREC 2025 track focuses on the recognition and segmentation of relief patterns embedded on the surface of a novel set of synthetically generated triangle meshes. Although the track garnered considerable interest, the problem remains open at the end of the track. In this report, we introduce a new 3D benchmark which was published to assess the performance of the most recent relief pattern recognition algorithms. We discuss the limitations of current techniques; the intrinsic challenges to face to address relief pattern analysis, and potential future research directions in this field.