A simple algorithm for extracting tree height in sparse scene from point cloud data.

Overview

TREE HEIGHT EXTRACTION IN SPARSE SCENES BASED ON UAV REMOTE SENSING

This is the offical python implementation of the paper "Tree Height Extraction in Sparse Scenes Based on UAV Remote Sensing," by Y. Liu, M. Xing, X. Zhou, Y. Song and D. Wang. For more information, checkout the IGARSS2020 oral paper.

Description

A simple algorithm for extracting tree height in sparse scene from point cloud data.

Requirements

  • python3
  • pip install numpy scipy matplotlib plotly
  • python-pcl

If you have python-pcl installation problems, you can try the ".whl" file in the floder "python-pcl/"

Tested Environment

I have tested the code in the following environment:

  • Windows10-64bit
  • Anaconda python3.7

Make sure anaconda has been installed on your windows computer, use the following command:

conda create -n py37 python=3.7
conda activate py37
pip install python_pcl-0.3.0rc1-cp37-cp37m-win_amd64.whl
conda install -c plotly plotly-orca==1.2.1 psutil requests
pip install numpy scipy matplotlib

ImportError: DLL load failed

If you meet "ImportError: DLL load failed" problem when you try to "import pcl", that was because "OpenNI2.dll" missing.

To fix this, I provide you with "python-pcl/OpenNI2.dll", just copy "OpenNI2.dll" into folder "YOUR_ANACONDA3_FLODER\envs\py37\lib\site-packages\pcl".

Run

python main.py

Citation

If you use this code or ideas from the paper for your research, please cite our paper:

@inproceedings{liu2020tree,
  title={Tree Height Extraction in Sparse Scenes Based on UAV Remote Sensing},
  author={Liu, Yuanzhong and Xing, Minfeng and Zhou, Xiaozhe and Song, Yang and Wang, Danyang},
  booktitle={IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium},
  pages={6499--6502},
  year={2020},
  organization={IEEE}
}
A PyTorch implementation of "DGC-Net: Dense Geometric Correspondence Network"

DGC-Net: Dense Geometric Correspondence Network This is a PyTorch implementation of our work "DGC-Net: Dense Geometric Correspondence Network" TL;DR A

191 Dec 16, 2022
Code for the paper "Multi-task problems are not multi-objective"

Multi-Task problems are not multi-objective This is the code for the paper "Multi-Task problems are not multi-objective" in which we show that the com

Michael Ruchte 5 Aug 19, 2022
Autolfads-tf2 - A TensorFlow 2.0 implementation of Latent Factor Analysis via Dynamical Systems (LFADS) and AutoLFADS

autolfads-tf2 A TensorFlow 2.0 implementation of LFADS and AutoLFADS. Installati

Systems Neural Engineering Lab 11 Oct 29, 2022
ROS-UGV-Control-Interface - Control interface which can be used in any UGV

ROS-UGV-Control-Interface Cam Closed: Cam Opened:

Ahmet Fatih Akcan 1 Nov 04, 2022
Exe-to-xlsm - Simple script to create VBscript of exe and inject to xlsm

🎁 Exe To Office Executable file injection to Office documents: .xlsm, .docm, .p

3 Jan 25, 2022
Implementation of ICLR 2020 paper "Revisiting Self-Training for Neural Sequence Generation"

Self-Training for Neural Sequence Generation This repo includes instructions for running noisy self-training algorithms from the following paper: Revi

Junxian He 45 Dec 31, 2022
PyTorch trainer and model for Sequence Classification

PyTorch-trainer-and-model-for-Sequence-Classification After cloning the repository, modify your training data so that the training data is a .csv file

NhanTieu 2 Dec 09, 2022
RGBD-Net - This repository contains a pytorch lightning implementation for the 3DV 2021 RGBD-Net paper.

[3DV 2021] We propose a new cascaded architecture for novel view synthesis, called RGBD-Net, which consists of two core components: a hierarchical depth regression network and a depth-aware generator

Phong Nguyen Ha 4 May 26, 2022
NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

880 Jan 07, 2023
An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" in Pytorch.

GLOM An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" for MNIST Dataset. To understand this

50 Oct 19, 2022
PyTorch implementation of Towards Accurate Alignment in Real-time 3D Hand-Mesh Reconstruction (ICCV 2021).

Towards Accurate Alignment in Real-time 3D Hand-Mesh Reconstruction Introduction This is official PyTorch implementation of Towards Accurate Alignment

TANG Xiao 96 Dec 27, 2022
ViDT: An Efficient and Effective Fully Transformer-based Object Detector

ViDT: An Efficient and Effective Fully Transformer-based Object Detector by Hwanjun Song1, Deqing Sun2, Sanghyuk Chun1, Varun Jampani2, Dongyoon Han1,

NAVER AI 262 Dec 27, 2022
An Implementation of SiameseRPN with Feature Pyramid Networks

SiameseRPN with FPN This project is mainly based on HelloRicky123/Siamese-RPN. What I've done is just add a Feature Pyramid Network method to the orig

3 Apr 16, 2022
FSL-Mate: A collection of resources for few-shot learning (FSL).

FSL-Mate is a collection of resources for few-shot learning (FSL). In particular, FSL-Mate currently contains FewShotPapers: a paper list which tracks

Yaqing Wang 1.5k Jan 08, 2023
Free-duolingo-plus - Duolingo account creator that uses your invite code to get you free duolingo plus

free-duolingo-plus duolingo account creator that uses your invite code to get yo

1 Jan 06, 2022
PyTorch implementation of the paper: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features

Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features Estimate the noise transition matrix with f-mutual information. This co

<a href=[email protected]"> 1 Jun 05, 2022
The coda and data for "Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach" (ACL '21)

We propose a hierarchical core-fringe learning framework to measure fine-grained domain relevance of terms – the degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., de

Jie Huang 14 Oct 21, 2022
Pytorch implementation of Bert and Pals: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning

PyTorch implementation of BERT and PALs Introduction Work by Asa Cooper Stickland and Iain Murray, University of Edinburgh. Code for BERT and PALs; mo

Asa Cooper Stickland 70 Dec 29, 2022
CAR-API: Cityscapes Attributes Recognition API

CAR-API: Cityscapes Attributes Recognition API This is the official api to download and fetch attributes annotations for Cityscapes Dataset. Content I

Kareem Metwaly 5 Dec 22, 2022