SelectAnyTree: A Promptable Instance Segmentation Model for 3D Forest LiDAR Point Clouds 文章
详细信息
- 来源站点
- ArXiv CS.CV
- 作者
- Trung Thanh Nguyen, Daniel Lusk, Kilian Gerberding, Janusch Vajna-Jehle, Tuan-Anh Vu, Duc Viet Le, Tu Vo, Phi Le Nguyen, Yasutomo Kawanishi, Takahiro Komamizu, Ichiro Ide, Julian Frey, Teja Kattenborn
- 文章类型
- PAPER
- 语言
- en
- 发布日期
- 2026-06-29
摘要
arXiv:2606.27491v1 Announce Type: new Abstract: Automated instance segmentation of forest LiDAR point clouds is increasingly critical as forest monitoring moves toward scalable, detailed, 3D measurement. Yet, progress is constrained by label scarcity for tree instances; a single hectare can hold millions of points and hundreds of overlapping, complex crowns, making manual annotation from scratch with raw data laborious and error-prone. Annotations are often corrected from automatic pre-segmentations, but remain costly as these provide no interactive or AI-assisted refinement. Inspired by the promptable paradigm of foundation segmentation models, we propose SelectAnyTree, a promptable instance segmentation model that delineates any individual tree in a 3D forest point cloud from a few clicks. It introduces two key components: Click-to-query prompt encoder and Canopy Height Model (CHM)-guided first prompt.