SelectAnyTree model enables promptable 3D forest LiDAR segmentation

Researchers have developed SelectAnyTree, a novel promptable instance segmentation model designed for 3D forest LiDAR point clouds. This model addresses the challenge of label scarcity in forest monitoring by enabling users to delineate individual trees with just a few clicks. SelectAnyTree incorporates a click-to-query prompt encoder and a Canopy Height Model (CHM)-guided first prompt, allowing for efficient and accurate tree segmentation even in complex, large-scale forest scenes. Evaluations across diverse forest regions demonstrate its strong generalization capabilities and superior performance compared to existing promptable baselines in terms of accuracy, efficiency, and parameter usage. AI

IMPACT Enhances forest monitoring capabilities with more efficient and accurate 3D tree segmentation.

RANK_REASON This is a research paper detailing a new model for a specific computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• alphaXiv
• arXiv
• Canopy Height Model (CHM) Derived From a TanDEM-X InSAR DSM and an Airborne Lidar DTM in Boreal Forest
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• lidar
• ScienceCast
• SelectAnyTree
• Trung Thanh Nguyen

• paper
• product

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