用自监督预训练提升点云树叶分割在不同林地和尺度下的稳定性。
Self-Supervised Pretraining Improves Cross-Site and Cross-Scale Robustness of Point Cloud Leaf-Wood Segmentation
- 在形状数据集上预训练点云模型，增强对不同树木的泛化能力。
- 针叶树木分割IoU提升至70.0%，阔叶树达76.3%，跨站点差异最小。
- 无需微调即可在单棵树与林地尺度保持高精度，适合野外复杂场景。
现有树木点云树叶分割方法在不同森林类型和站点间性能波动较大。尽管点云自监督学习（SSL）已提升林业点云任务中深度模型的泛化能力，如生物量回归与单木分割，但其在树叶分割中的应用尚未验证。本研究在ShapeNet-55基础上，加入2,400个个体树木点云，对广泛使用的点云SSL架构Point-M2AE进行预训练。微调与推理时采用递归体素细分处理点密度差异，使同一模型可在单木与林地尺度下运行而无需修改架构。相比无预训练模型，预训练后针叶树木分割的IoU从60.5%提升至70.0%，阔叶树从69.7%提升至76.3%。在横跨三个气候带、四个国家的基准测试中，该模型表现最优且跨站点变异最小。林地级分割保持高精度，阔叶林与针叶林的mIoU分别为84.7%和77.7%，表明模型具备跨尺度泛化能力。在热带雨林中，针对密集树冠带来的挑战，将该模型与结构量化模型结合，对圭亚那、印度尼西亚和秘鲁共28棵树的木材体积进行估计，结果误差最低（MAE = 2.40 m³），不足算法基线（LeWos: 5.94 m³；CWLS: 5.27 m³）的一半。
原文摘要 · Abstract (English)
The accuracy of existing leaf-wood segmentation methods for tree point clouds varies across forest types and sites. Self-supervised learning (SSL) on point clouds has improved the generalization of deep learning models for forestry point cloud tasks, including biomass regression and individual tree segmentation, but its applicability to leaf-wood segmentation remains untested. In this study, we pretrained Point-M2AE, a widely used SSL architecture for point clouds, on ShapeNet-55 augmented with 2,400 individual tree point clouds. For fine-tuning and inference, we used recursive voxel subdivision to handle the wide variation in point density across inputs, allowing the same model to operate at both individual-tree and plot scales without architecture change. Compared to the model without pretraining, the pretrained model improved wood IoU from 60.5% to 70.0% for needleleaf and from 69.7% to 76.3% for broadleaf trees. On a benchmark spanning four countries across three climatic zones, the pretrained model achieved the smallest cross-site variation and highest overall performance among compared methods (LeWos, CWLS, and PointTransformer). Plot-level segmentation maintained accuracy comparable to individual-tree performance, with mIoU of 84.7% for broadleaf and 77.7% for needleleaf plots, showing that the model generalizes across scales without additional finetuning. As a downstream test in tropical forests, where dense canopies make segmentation challenging, we applied our model and a quantitative structure model to estimate wood volume for 28 trees from Guyana, Indonesia, and Peru to assess whether the segmentation improvements from SSL pretraining translate into improved downstream performance. The resulting volume estimates achieved the lowest error among all methods tested (MAE = 2.40 m$^3$), less than half that of algorithmic baselines (LeWos: 5.94 m$^3$; CWLS: 5.27 m$^3$).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。