Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.05400 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917112917786624 |
|---|---|
| author | Li, Sihang Jiang, Zeyu Chen, Grace Xu, Chenyang Tan, Siqi Wang, Xue Fang, Irving Zyskowski, Kristof McPherron, Shannon P. Iovita, Radu Feng, Chen Zhang, Jing |
| author_facet | Li, Sihang Jiang, Zeyu Chen, Grace Xu, Chenyang Tan, Siqi Wang, Xue Fang, Irving Zyskowski, Kristof McPherron, Shannon P. Iovita, Radu Feng, Chen Zhang, Jing |
| contents | 3D reassembly is a challenging spatial intelligence task with broad applications across scientific domains. While large-scale synthetic datasets have fueled promising learning-based approaches, their generalizability to different domains is limited. Critically, it remains uncertain whether models trained on synthetic datasets can generalize to real-world fractures where breakage patterns are more complex. To bridge this gap, we propose GARF, a generalizable 3D reassembly framework for real-world fractures. GARF leverages fracture-aware pretraining to learn fracture features from individual fragments, with flow matching enabling precise 6-DoF alignments. At inference time, we introduce one-step preassembly, improving robustness to unseen objects and varying numbers of fractures. In collaboration with archaeologists, paleoanthropologists, and ornithologists, we curate Fractura, a diverse dataset for vision and learning communities, featuring real-world fracture types across ceramics, bones, eggshells, and lithics. Comprehensive experiments have shown our approach consistently outperforms state-of-the-art methods on both synthetic and real-world datasets, achieving 82.87\% lower rotation error and 25.15\% higher part accuracy. This sheds light on training on synthetic data to advance real-world 3D puzzle solving, demonstrating its strong generalization across unseen object shapes and diverse fracture types. GARF's code, data and demo are available at https://ai4ce.github.io/GARF/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05400 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GARF: Learning Generalizable 3D Reassembly for Real-World Fractures Li, Sihang Jiang, Zeyu Chen, Grace Xu, Chenyang Tan, Siqi Wang, Xue Fang, Irving Zyskowski, Kristof McPherron, Shannon P. Iovita, Radu Feng, Chen Zhang, Jing Computer Vision and Pattern Recognition Artificial Intelligence 3D reassembly is a challenging spatial intelligence task with broad applications across scientific domains. While large-scale synthetic datasets have fueled promising learning-based approaches, their generalizability to different domains is limited. Critically, it remains uncertain whether models trained on synthetic datasets can generalize to real-world fractures where breakage patterns are more complex. To bridge this gap, we propose GARF, a generalizable 3D reassembly framework for real-world fractures. GARF leverages fracture-aware pretraining to learn fracture features from individual fragments, with flow matching enabling precise 6-DoF alignments. At inference time, we introduce one-step preassembly, improving robustness to unseen objects and varying numbers of fractures. In collaboration with archaeologists, paleoanthropologists, and ornithologists, we curate Fractura, a diverse dataset for vision and learning communities, featuring real-world fracture types across ceramics, bones, eggshells, and lithics. Comprehensive experiments have shown our approach consistently outperforms state-of-the-art methods on both synthetic and real-world datasets, achieving 82.87\% lower rotation error and 25.15\% higher part accuracy. This sheds light on training on synthetic data to advance real-world 3D puzzle solving, demonstrating its strong generalization across unseen object shapes and diverse fracture types. GARF's code, data and demo are available at https://ai4ce.github.io/GARF/. |
| title | GARF: Learning Generalizable 3D Reassembly for Real-World Fractures |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2504.05400 |