Completing 3D point clouds of individual trees using deep learning
Year: 2024
Type: article
Abstract: This dataset is currently kept restricted for double anonymous peer review. This repository contains data associated with the study "Completing 3D point clouds of individual trees using deep learning"and accompanies the code available at https://anonymous.4open.science/r/treePoinTr-615A/README.md DATA & FILE OVERVIEW treePoinTr_rawdata TheGrove_obj: 3D mesh files in .obj format, created using the Blender add-on "The Grove"TheGrove_pointclouds: point clouds derived from TheGrove_obj by random sampling on mesh surfaceSapTreeGen_obj: 3D mesh files in .obj format, created using the Blender add-on "Sapling Tree Gen"SapTreeGen_pointclouds: point clouds derived from SapTreeGen_obj by random sampling on mesh surfaceHeliosSim: HELIOS++ simulations on TheGrove objects. 12 scan positions (legs) per tree object, poit clouds in .xyz format.realTLS_BLK360 trees: 12 individual tree leaf-off point clouds, acquired with a Leica BLK360 TLS. Branches < 5cm were removed.realTLS_Riegl-VZ400_segments: Segments from forest point clouds acquired with a Riegl-VZ400i TLS. Areas were manually segmented for completeness. treePoinTr_trainedModels PoinTr models fine-tuned on the datasets with the same name.The file ckpt-best.pth contains the best performing model weights and should be used for inference.
Author: Bornand, Aline
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930037248
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