このリポジトリは、「自動化された森林インベントリ: 3D ディープラーニングによる高密度空中 LiDAR 点群の分析」というタイトルの論文の公式コードを表します。
以前のリポジトリを参照してください。
https://github.com/prs-eth/PanopticSegForLargeScalePointCloud
これには、詳細な手順と、発生する可能性はあるがすでに解決されている問題が含まれています。
古い RAW ファイルを新しい RAW ファイルに置き換えてください。
たとえば、data_set1_5classes には、論文の表 4 の「基本設定」のデータが含まれています。
ダウンロードできます:
cd / $YOURPATH $/ForAINet/PointCloudSegmentation # Command for training
python train.py task=panoptic data=panoptic/treeins_set1 models=panoptic/FORpartseg_3heads model_name=PointGroup-PAPER training=treeins_set1 job_name= # YOUR_JOB_NAME# # Command for training
python train.py task=panoptic data=panoptic/treeins_set1 models=panoptic/FORpartseg_3heads_BiLoss model_name=PointGroup-PAPER training=treeins_set1_addBiLoss job_name= # YOUR_JOB_NAME# # Command for training
python train.py task=panoptic data=panoptic/treeins_set1_classweight models=panoptic/FORpartseg_3heads model_name=PointGroup-PAPER training=treeins_set1_nw8_classweight job_name= # YOUR_JOB_NAME# # Command for training
python train.py task=panoptic data=panoptic/treeins_set1_classweight models=panoptic/FORpartseg_3heads_heightweight model_name=PointGroup-PAPER training=treeins_set1_heightweight job_name= # YOUR_JOB_NAME# # Command for training
# To be added # Command for training
python train.py task=panoptic data=panoptic/treeins_set1_add_intensity models=panoptic/FORpartseg_3heads model_name=PointGroup-PAPER training=treeins_set1_intensity job_name= # YOUR_JOB_NAME# # Command for training
python train.py task=panoptic data=panoptic/treeins_set1_add_return_num models=panoptic/FORpartseg_3heads model_name=PointGroup-PAPER training=treeins_set1_return_num job_name= # YOUR_JOB_NAME# # Command for training
python train.py task=panoptic data=panoptic/treeins_set1_add_scan_angle_rank models=panoptic/FORpartseg_3heads model_name=PointGroup-PAPER training=treeins_set1_scan_angle_rank job_name= # YOUR_JOB_NAME# # Command for training
python train.py task=panoptic data=panoptic/treeins_set1_add_all_20010 models=panoptic/FORpartseg_3heads model_name=PointGroup-PAPER training=treeins_set1_addallFea_20010 job_name= # YOUR_JOB_NAME# # Command for training
python train.py task=panoptic data=panoptic/treeins_set1_curved_subsam models=panoptic/FORpartseg_3heads model_name=PointGroup-PAPER training=treeins_set1_addCurvedSubsample job_name= # YOUR_JOB_NAME# # Command for training
python train.py task=panoptic data=panoptic/treeins_set1_treemix3d models=panoptic/FORpartseg_3heads model_name=PointGroup-PAPER training=treeins_set1_mixtree job_name= # YOUR_JOB_NAME# # Command for training
python train.py task=panoptic data=panoptic/treeins_set1_treemix3d_pd#POINT_DENSITY# models=panoptic/FORpartseg_3heads model_name=PointGroup-PAPER training=mixtree_#POINT_DENSITY# job_name= # YOUR_JOB_NAME#
# take point density=10 as an example
python train.py task=panoptic data=panoptic/treeins_set1_treemix3d_pd10 models=panoptic/FORpartseg_3heads model_name=PointGroup-PAPER training=mixtree_10 job_name= # YOUR_JOB_NAME#事前トレーニングされたモデルはここからダウンロードできます: https://polybox.ethz.ch/index.php/s/aUypOXxOW3pYPe9
# Command for test
# remember to change the following 2 parameters in eval.yaml:
# 1. "checkpoint_dir" to your log files path
# 2. "data" is the paths for your test files
python eval.py
# Command for output the final evaluation file
# replace parameter "test_sem_path" by your path
python evaluation_stats_FOR.py cd / $YOURPATH $/ForAINet/tree_metrics
# remember to adjust parameters based on your dataset
python measurement.py # Please note that our code is based on the Superpoint Graphs repository, which can be found at https://github.com/loicland/superpoint_graph. We have included our custom partition_FORdata.py file.
cd / $YOURPATH $/ForAINet/superpoint_graph/partition
python partition_FORdata.py私たちの成果が役立つと思われる場合は、遠慮なく引用してください。
@article{
xiang2024automated,
title={Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning},
author={Binbin Xiang, Maciej Wielgosz, Theodora Kontogianni, Torben Peters, Stefano Puliti, Rasmus Astrup, Konrad Schindler},
journal={Remote Sensing of Environment},
volume={305},
pages={114078},
year={2024},
publisher={Elsevier}
}
