Granularity-Aware Transfer for Tree Instance Segmentation in Synthetic and Real Forests

2026-04-15

AI Summary

This work addresses the domain shift challenge in forest perception arising from the disparity between synthetic data with fine-grained annotations (e.g., trunk and crown) and real-world data limited to coarse-grained tree-level labels. To this end, the authors introduce the Mixed-Granularity Tree Dataset (MGTD) and a four-stage evaluation protocol. The core contribution is a granularity-aware knowledge distillation mechanism that effectively transfers structural priors from a synthetic-domain teacher model to a real-domain student model trained solely on coarse labels, via logit-space fusion and mask unification strategies. By integrating knowledge distillation, instance segmentation, and multi-granularity alignment, the proposed method significantly improves mask AP in tree instance segmentation—particularly for small and distant trees—and establishes a new benchmark for Sim2Real transfer under label granularity constraints.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Segmentation
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• synthetic-to-real transfer
• label granularity mismatch
• tree instance segmentation
• domain shift
• forestry perception

Innovation

• granularity-aware distillation
• synthetic-to-real transfer
• tree instance segmentation
• mixed-granularity dataset
• mask unification