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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.13722 |
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| _version_ | 1866911595260542976 |
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| author | Deoli, Pankaj Tej, Atef Ashri, Anmol JS, Anandatirtha Berns, Karsten |
| author_facet | Deoli, Pankaj Tej, Atef Ashri, Anmol JS, Anandatirtha Berns, Karsten |
| contents | We address the challenge of synthetic-to-real transfer in forestry perception where real data have only coarse Tree labels while synthetic data provide fine-grained trunk/crown annotations. We introduce MGTD, a mixed-granularity dataset with 53k synthetic and 3.6k real images, and a four-stage protocol isolating domain shift and granularity mismatch. Our core contribution is granularity-aware distillation, which transfers structural priors from fine-grained synthetic teachers to a coarse-label student via logit-space merging and mask unification. Experiments show consistent mask AP gains, especially for small/distant trees, establishing a testbed for Sim-Real transfer under label granularity constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13722 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Granularity-Aware Transfer for Tree Instance Segmentation in Synthetic and Real Forests Deoli, Pankaj Tej, Atef Ashri, Anmol JS, Anandatirtha Berns, Karsten Computer Vision and Pattern Recognition We address the challenge of synthetic-to-real transfer in forestry perception where real data have only coarse Tree labels while synthetic data provide fine-grained trunk/crown annotations. We introduce MGTD, a mixed-granularity dataset with 53k synthetic and 3.6k real images, and a four-stage protocol isolating domain shift and granularity mismatch. Our core contribution is granularity-aware distillation, which transfers structural priors from fine-grained synthetic teachers to a coarse-label student via logit-space merging and mask unification. Experiments show consistent mask AP gains, especially for small/distant trees, establishing a testbed for Sim-Real transfer under label granularity constraints. |
| title | Granularity-Aware Transfer for Tree Instance Segmentation in Synthetic and Real Forests |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.13722 |