Semantic 3D Skeleton Extraction for Precision Agricultural Robotics : Preliminary Result

Abstract

A multi-modal dataset was constructed in a real orchard environment under leaf-off conditions using an RGB-D camera and LiDAR, enabling clear observation of branch and trunk structures. The complementary geometric information from both sensors allows for more precise 3D structural reconstruction. Dense point clouds obtained from the RGB-D camera are fused with LiDAR point clouds via ICP registration, followed by ground removal and DBSCAN clustering to segment individual trees. AdTree is then applied to each segmented tree to extract the 3D skeletal structure and generate Ground Truth. The constructed GT explicitly represents the hierarchical branch structure of each tree, and additional data collection under leaf-on conditions is planned to enable quantitative evaluation of skeleton extraction performance across varying foliage conditions. Furthermore, the constructed dataset will be utilized for training and evaluation of a Flow Matching-based generative model for tree skeletonization. Flow Matching enables stable skeleton reconstruction even from noisy and heavily occluded point clouds in real orchard environments, and the dataset is expected to facilitate quantitative analysis of performance differences between leaf-off and leaf-on conditions.