PointNLM: Point Nonlocal-Means for vegetation segmentation based on middle echo point clouds

06/20/2019
by   Jonathan Li, et al.
3

Middle-echo, which covers one or a few corresponding points, is a specific type of 3D point cloud acquired by a multi-echo laser scanner. In this paper, we propose a novel approach for automatic segmentation of trees that leverages middle-echo information from LiDAR point clouds. First, using a convolution classification method, the proposed type of point clouds reflected by the middle echoes are identified from all point clouds. The middle-echo point clouds are distinguished from the first and last echoes. Hence, the crown positions of the trees are quickly detected from the huge number of point clouds. Second, to accurately extract trees from all point clouds, we propose a 3D deep learning network, PointNLM, to semantically segment tree crowns. PointNLM captures the long-range relationship between the point clouds via a non-local branch and extracts high-level features via max-pooling applied to unordered points. The whole framework is evaluated using the Semantic 3D reduced-test set. The IoU of tree point cloud segmentation reached 0.864. In addition, the semantic segmentation network was tested using the Paris-Lille-3D dataset. The average IoU outperformed several other popular methods. The experimental results indicate that the proposed algorithm provides an excellent solution for vegetation segmentation from LiDAR point clouds.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 7

page 17

page 19

page 21

page 22

page 25

page 26

07/30/2020

Cascaded Non-local Neural Network for Point Cloud Semantic Segmentation

In this paper, we propose a cascaded non-local neural network for point ...
12/26/2020

Assigning Apples to Individual Trees in Dense Orchards using 3D Color Point Clouds

We propose a 3D color point cloud processing pipeline to count apples on...
10/15/2016

Road Curb Extraction from Mobile LiDAR Point Clouds

Automatic extraction of road curbs from uneven, unorganized, noisy and m...
06/09/2020

Tree Annotations in LiDAR Data Using Point Densities and Convolutional Neural Networks

LiDAR provides highly accurate 3D point clouds. However, data needs to b...
06/22/2018

Point cloud segmentation using hierarchical tree for architectural models

Recent developments in the 3D scanning technologies have made the genera...
03/06/2017

An optimal hierarchical clustering approach to segmentation of mobile LiDAR point clouds

This paper proposes a hierarchical clustering approach for the segmentat...
07/21/2017

3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds

Semantic parsing of large-scale 3D point clouds is an important research...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.