SelectAnyTree: A Promptable Instance Segmentation Model for 3D Forest LiDAR Point Clouds
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
Abstract
Instance segmentation of trees in forest LiDAR point clouds is constrained by label scarcity: A single hectare holds millions of points and hundreds of overlapping tree crowns, making manual annotation laborious, while automatic pre-segmentations offer no interactive refinement. Inspired by the promptable paradigm of foundation segmentation models, we propose SelectAnyTree, which delineates any individual tree in a 3D forest point cloud from a few clicks and is purpose-built for promptable instance segmentation of 3D forest LiDAR scenes. The proposed SelectAnyTree couples three lightweight stages: (1) Sparse voxel scene encoder that embeds the forest once into reusable features, (2) Click-to-query prompt encoder that turns each click into a single content query from its 3D position, positive/negative polarity, and the backbone feature of its nearest voxel, and (3) State-space query decoder that converts this query into one tree mask with linear-time complexity, with a mask feedback that conditions each refinement round on the previous mask. Each additional tree therefore costs only a lightweight prompt-encoding and decoding pass, and the full model requires just 19.4 M parameters, far fewer than prior promptable 3D models. Additionally, we exploit forest-aware information by detecting treetops as local maxima of the Canopy Height Model (CHM) computed from the scene geometry, and associating one with the user's click as a free initial click. Across seven diverse forest regions and an independent held-out dataset, SelectAnyTree segments a target tree to 79.9 Intersection-over-Union (IoU) from a single click, 24.7 points above the strongest promptable baseline, and reaches every accuracy target with the fewest clicks. The source code is available at https://github.com/thanhhff/SelectAnyTree.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.