scholarly journals Building Extraction from Airborne Multi-Spectral LiDAR Point Clouds Based on Graph Geometric Moments Convolutional Neural Networks

2020 ◽  
Vol 12 (19) ◽  
pp. 3186 ◽  
Author(s):  
Dilong Li ◽  
Xin Shen ◽  
Yongtao Yu ◽  
Haiyan Guan ◽  
Jonathan Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Point Cloud ◽  
Point Clouds ◽  
Hierarchical Architecture ◽  
Building Extraction ◽  
Cloud Data ◽  
Geometric Moments ◽  
Point Cloud Segmentation ◽  
Previous State

Building extraction has attracted much attentions for decades as a prerequisite for many applications and is still a challenging topic in the field of photogrammetry and remote sensing. Due to the lack of spectral information, massive data processing, and approach universality, building extraction from point clouds is still a thorny and challenging problem. In this paper, a novel deep-learning-based framework is proposed for building extraction from point cloud data. Specifically, first, a sample generation method is proposed to split the raw preprocessed multi-spectral light detection and ranging (LiDAR) data into numerous samples, which are directly fed into convolutional neural networks and completely cover the original inputs. Then, a graph geometric moments (GGM) convolution is proposed to encode the local geometric structure of point sets. In addition, a hierarchical architecture equipped with GGM convolution, called GGM convolutional neural networks, is proposed to train and recognize building points. Finally, the test scenes with varying sizes can be fed into the framework and obtain a point-wise extraction result. We evaluate the proposed framework and methods on the airborne multi-spectral LiDAR point clouds collected by an Optech Titan system. Compared with previous state-of-the-art networks, which are designed for point cloud segmentation, our method achieves the best performance with a correctness of 95.1%, a completeness of 93.7%, an F-measure of 94.4%, and an intersection over union (IoU) of 89.5% on two test areas. The experimental results confirm the effectiveness and efficiency of the proposed framework and methods.

scholarly journals SEGMENTATION OF LARGE UNSTRUCTURED POINT CLOUDS USING OCTREE-BASED REGION GROWING AND CONDITIONAL RANDOM FIELDS

2017 ◽  
Vol XLII-2/W8 ◽  
pp. 25-30 ◽  
Author(s):  
M. Bassier ◽  
M. Bonduel ◽  
B. Van Genechten ◽  
M. Vergauwen
Keyword(s):  
Random Fields ◽  
Point Cloud ◽  
Conditional Random Fields ◽  
Conditional Random Field ◽  
Region Growing ◽  
Point Clouds ◽  
Single Type ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Point Cloud Segmentation

Point cloud segmentation is a crucial step in scene understanding and interpretation. The goal is to decompose the initial data into sets of workable clusters with similar properties. Additionally, it is a key aspect in the automated procedure from point cloud data to BIM. Current approaches typically only segment a single type of primitive such as planes or cylinders. Also, current algorithms suffer from oversegmenting the data and are often sensor or scene dependent.<br><br> In this work, a method is presented to automatically segment large unstructured point clouds of buildings. More specifically, the segmentation is formulated as a graph optimisation problem. First, the data is oversegmented with a greedy octree-based region growing method. The growing is conditioned on the segmentation of planes as well as smooth surfaces. Next, the candidate clusters are represented by a Conditional Random Field after which the most likely configuration of candidate clusters is computed given a set of local and contextual features. The experiments prove that the used method is a fast and reliable framework for unstructured point cloud segmentation. Processing speeds up to 40,000 points per second are recorded for the region growing. Additionally, the recall and precision of the graph clustering is approximately 80%. Overall, nearly 22% of oversegmentation is reduced by clustering the data. These clusters will be classified and used as a basis for the reconstruction of BIM models.

scholarly journals Panicle-3D: Efficient Phenotyping Tool for Precise Semantic Segmentation of Rice Panicle Point Cloud

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Liang Gong ◽  
Xiaofeng Du ◽  
Kai Zhu ◽  
Ke Lin ◽  
Qiaojun Lou ◽  
...  
Keyword(s):  
Point Cloud ◽  
Network Models ◽  
Semantic Segmentation ◽  
Spatial Location ◽  
Point Clouds ◽  
Utilization Efficiency ◽  
Data Set ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Segmentation Accuracy

The automated measurement of crop phenotypic parameters is of great significance to the quantitative study of crop growth. The segmentation and classification of crop point cloud help to realize the automation of crop phenotypic parameter measurement. At present, crop spike-shaped point cloud segmentation has problems such as fewer samples, uneven distribution of point clouds, occlusion of stem and spike, disorderly arrangement of point clouds, and lack of targeted network models. The traditional clustering method can realize the segmentation of the plant organ point cloud with relatively independent spatial location, but the accuracy is not acceptable. This paper first builds a desktop-level point cloud scanning apparatus based on a structured-light projection module to facilitate the point cloud acquisition process. Then, the rice ear point cloud was collected, and the rice ear point cloud data set was made. In addition, data argumentation is used to improve sample utilization efficiency and training accuracy. Finally, a 3D point cloud convolutional neural network model called Panicle-3D was designed to achieve better segmentation accuracy. Specifically, the design of Panicle-3D is aimed at the multiscale characteristics of plant organs, combined with the structure of PointConv and long and short jumps, which accelerates the convergence speed of the network and reduces the loss of features in the process of point cloud downsampling. After comparison experiments, the segmentation accuracy of Panicle-3D reaches 93.4%, which is higher than PointNet. Panicle-3D is suitable for other similar crop point cloud segmentation tasks.

scholarly journals A COMPARATIVE STUDY OF POINT CLOUDS SEMANTIC SEGMENTATION USING THREE DIFFERENT NEURAL NETWORKS ON THE RAILWAY STATION DATASET

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 223-228
Author(s):  
Y. A. Lumban-Gaol ◽  
Z. Chen ◽  
M. Smit ◽  
X. Li ◽  
M. A. Erbaşu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Point Cloud ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Railway Station ◽  
Train Station ◽  
Cloud Data ◽  
Indoor Scenes ◽  
Segmentation Problem

Abstract. Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the point cloud semantic segmentation problem. This paper evaluates three different neural networks for semantic segmentation of point clouds, namely PointNet++, PointCNN and DGCNN. A public indoor scene of the Amersfoort railway station is used as the study area. Unlike the typical indoor scenes and even more from the ubiquitous outdoor ones in currently available datasets, the station consists of objects such as the entrance gates, ticket machines, couches, and garbage cans. For the experiment, we use subsets from the data, remove the noise, evaluate the performance of the selected neural networks. The results indicate an overall accuracy of more than 90% for all the networks but vary in terms of mean class accuracy and mean Intersection over Union (IoU). The misclassification mainly occurs in the classes of couch and garbage can. Several factors that may contribute to the errors are analyzed, such as the quality of the data and the proportion of the number of points per class. The adaptability of the networks is also heavily dependent on the training location: the overall characteristics of the train station make a trained network for one location less suitable for another.

scholarly journals Using three dimensional convolutional neural networks for denoising echosounder point cloud data

2020 ◽  
Vol 5 ◽  
pp. 100016 ◽  
Author(s):  
David Stephens ◽  
Andrew Smith ◽  
Thomas Redfern ◽  
Andrew Talbot ◽  
Andrew Lessnoff ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Cloud Data

Supervoxel-Based Region Growing Segmentation for Point Cloud Data

2020 ◽  
pp. 2154007
Author(s):  
Nan Luo ◽  
Yuanyuan Jiang ◽  
Quan Wang
Keyword(s):  
Point Cloud ◽  
High Efficiency ◽  
Region Growing ◽  
Experimental Tests ◽  
Point Clouds ◽  
Spatial Distance ◽  
Local Geometry ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Region Growing Segmentation

Point cloud segmentation is a crucial fundamental step in 3D reconstruction, object recognition and scene understanding. This paper proposes a supervoxel-based point cloud segmentation algorithm in region growing principle to solve the issues of inaccurate boundaries and nonsmooth segments in the existing methods. To begin with, the input point cloud is voxelized and then pre-segmented into sparse supervoxels by flow constrained clustering, considering the spatial distance and local geometry between voxels. Afterwards, plane fitting is applied to the over-segmented supervoxels and seeds for region growing are selected with respect to the fitting residuals. Starting from pruned seed patches, adjacent supervoxels are merged in region growing style to form the final segments, according to the normalized similarity measure that integrates the smoothness and shape constraints of supervoxels. We determine the values of parameters via experimental tests, and the final results show that, by voxelizing and pre-segmenting the point clouds, the proposed algorithm is robust to noises and can obtain smooth segmentation regions with accurate boundaries in high efficiency.

scholarly journals Benchmarking Deep Learning Models on Point Cloud Segmentation

2021 ◽  
Author(s):  
Thanasis Zoumpekas ◽  
Guillem Molina ◽  
Maria Salamó ◽  
Anna Puig
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Performance Metrics ◽  
Point Clouds ◽  
Learning Models ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Segmentation Analysis ◽  
Intelligent Analysis ◽  
Segmentation Task

Point clouds are currently used for a variety of applications, such as detection tasks in medical and geological domains. Intelligent analysis of point clouds is considered a highly computationally demanding and challenging task, especially the segmentation task among the points. Although numerous deep learning models have recently been proposed to segment point cloud data, there is no clear instruction of which exactly neural network to utilize and then incorporate into a system dealing with point cloud segmentation analysis. Besides, the majority of the developed models emphasize more on the accuracy rather than the efficiency, in order to achieve great results. Consequently, the training, validation and testing phases of the models require a great number of processing hours and a huge amount of memory. These high computational requirements are commonly difficult to deal with for many users. In this article, we analyse five state-of-the-art deep learning models for part segmentation task and give meaningful insights into the utilization of each one. We advance guidelines based on different properties, considering both learning-related metrics, such as accuracy, and system-related metrics, such as run time and memory footprint. We further propose and analyse generalized performance metrics, which facilitate the model evaluation phase in segmentation tasks allowing users to select the most appropriate approach for their context in terms of accuracy and efficiency.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

Sign in / Sign up

Export Citation Format

Share Document