一个无监督的语义细分网络,用于在3D点云中的木叶分离.
Yijun Zhong1, Jiaohua Qin1, Shuai Liu1
1Central South University of Forestry and Technology, Changsha, 410004, China.
Plant phenomics (Washington, D.C.)
|December 19, 2025
概括
本研究介绍了一种无监督的方法,用于在3D树点云中将木材和叶子组件分开,从而消除了手动数据注释的需要. 新型网络实现了具有竞争力的准确性,推进了自动化森林库存.
科学领域:
- 林业和远程传感 林业和远程传感
- 计算机视觉和机器学习
背景情况:
- 自动化森林库存和管理依赖于在树点云中准确分离木材和叶子组件.
- 传统的监督方法需要广泛,昂贵和耗时的点对点注释,这阻碍了广泛采用.
- 需要无监督方法来克服木叶分离中监督学习的局限性.
研究的目的:
- 探索3D树点云中无监督木叶分离的可行性.
- 提出和评估一种新的无监督语义细分网络,用于直接提取木材和叶子组件.
主要方法:
- 开发了一个无监督的语义细分网络,利用稀疏的卷积神经网络骨干.
- 集成的双点注意 (DPA) 和点云功能卷积集成器 (PFCI) 模块,用于增强功能提取和融合.
- 通过超点聚类生成伪标签,用于语义分类.
主要成果:
- 实现了67.583%的整体准确率 (oAcc) 和38.512%的森林层次木叶分离的平均交叉点 (mIoU).
- 在树层木材和叶子分离方面,获得了80.856%的oAcc和49.695%的mIoU.
- 超越了最先进的方法 (GrowSP,PointDC) 的性能,并且在封闭和强大的概括能力下表现出了稳健性.
结论:
- 拟议的无监督网络在3D点云中有效地将木材和叶子组件分开,没有注释数据.
- DPA和PFCI模块显著有助于提高细分精度.
- 这种方法为自动化森林库存和管理提供了可行的和强大的解决方案.
相关概念视频
Softwoods and Hardwoods
469
Softwoods and hardwoods, derived from different types of trees, are distinguished by their leaf structures and cellular compositions, each serving unique purposes in construction and manufacturing. Softwoods come from cone-bearing trees with needle-like leaves and are predominantly composed of longitudinal cells called tracheids and a smaller proportion of radial cells known as rays. Due to their cellular structure, softwoods are commonly used in construction for structural frames, sheathing,...
469
Structural Classification of Joints
6.9K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
A fibrous joint is where the adjacent bones are united by fibrous connective...
6.9K


