Estimativa de diâmetro de troncos de eucalipto a partir de nuvens de pontos LiDAR de smartphone e Redes Neurais Profundas

Abstract

Accurate measurement of dendrometric parameters, particularly the Diameter at Breast Height (DBH), is fundamental for forest inventory and sustainable management. However, traditional field methods are labor-intensive, timeconsuming, and prone to error. The integration of LiDAR sensors into consumer-grade smartphones offers a scalable and cost-effective alternative, yet requires robust computational methods to process the resulting 3D point cloud data. This thesis presents a novel, end-to-end framework for the automated measurement of DBH in eucalyptus trees by leveraging deep learning for 3D reconstruction and semantic segmentation. We develop and validate two distinct processing pipelines: one that reconstructs complete 360- degree tree models from rapid, partial scans using a point cloud completion architecture, and another that processes full-circle scans captured directly in the field. Both workflows converge into a shared segmentation stage where a PointTransformer network performs high-fidelity semantic segmentation to precisely isolate the DBH region from the tree stem. An automated algorithm then calculates the diameter from the segmented point cloud. Our results demonstrate that the proposed framework achieves high precision, with measurements showing no statistically significant difference from manual caliper methods. The direct-scan pipeline proved superior, achieving a RMSE of less than 0.55 cm across all tree classes. Critically, the methodology yields a transformative improvement in operational efficiency, reducing in-field data collection time by three to thirty-fold. By validating a high-precision, low-cost workflow, this research provides a significant step toward the automation of forest inventories, enabling more efficient and data-driven practices in precision forestry. To foster future research, two novel datasets containing 880 annotated tree scans are also made publicly available.