scholarly journals DNet: Dynamic Neighborhood Feature Learning in Point Cloud

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2327
Author(s):  
Fujing Tian ◽  
Zhidi Jiang ◽  
Gangyi Jiang
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Feature Learning ◽  
Learning Networks ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Neighborhood Selection ◽  
Public Datasets ◽  
The Relationship ◽  
Region Feature

Neighborhood selection is very important for local region feature learning in point cloud learning networks. Different neighborhood selection schemes may lead to quite different results for point cloud processing tasks. The existing point cloud learning networks mainly adopt the approach of customizing the neighborhood, without considering whether the selected neighborhood is reasonable or not. To solve this problem, this paper proposes a new point cloud learning network, denoted as Dynamic neighborhood Network (DNet), to dynamically select the neighborhood and learn the features of each point. The proposed DNet has a multi-head structure which has two important modules: the Feature Enhancement Layer (FELayer) and the masking mechanism. The FELayer enhances the manifold features of the point cloud, while the masking mechanism is used to remove the neighborhood points with low contribution. The DNet can learn the manifold features and spatial geometric features of point cloud, and obtain the relationship between each point and its effective neighborhood points through the masking mechanism, so that the dynamic neighborhood features of each point can be obtained. Experimental results on three public datasets demonstrate that compared with the state-of-the-art learning networks, the proposed DNet shows better superiority and competitiveness in point cloud processing task.

scholarly journals An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios

2021 ◽  
Vol 13 (10) ◽  
pp. 1985
Author(s):  
Emre Özdemir ◽  
Fabio Remondino ◽  
Alessandro Golkar
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Computational Power ◽  
Specific Data ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Current State ◽  
Data Source ◽  
Point Cloud Classification ◽  
And Training

With recent advances in technologies, deep learning is being applied more and more to different tasks. In particular, point cloud processing and classification have been studied for a while now, with various methods developed. Some of the available classification approaches are based on specific data source, like LiDAR, while others are focused on specific scenarios, like indoor. A general major issue is the computational efficiency (in terms of power consumption, memory requirement, and training/inference time). In this study, we propose an efficient framework (named TONIC) that can work with any kind of aerial data source (LiDAR or photogrammetry) and does not require high computational power while achieving accuracy on par with the current state of the art methods. We also test our framework for its generalization ability, showing capabilities to learn from one dataset and predict on unseen aerial scenarios.

scholarly journals Airborne LiDAR Point Cloud Processing for Archaeology. Pipeline and QGIS Toolbox

2021 ◽  
Vol 13 (16) ◽  
pp. 3225
Author(s):  
Benjamin Štular ◽  
Stefan Eichert ◽  
Edisa Lozić
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
State Of The Art ◽  
Airborne Lidar ◽  
Reproducible Research ◽  
Lidar Data ◽  
Archaeological Prospection ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Airborne Lidar Data

The use of topographic airborne LiDAR data has become an essential part of archaeological prospection. However, as a step towards theoretically aware, impactful, and reproducible research, a more rigorous and transparent method of data processing is required. To this end, we set out to create a processing pipeline for archaeology-specific point cloud processing and derivation of products that are optimized for general-purpose data. The proposed pipeline improves on ground and building point cloud classification. The main area of innovation in the proposed pipeline is raster grid interpolation. We have improved the state-of-the-art by introducing a hybrid interpolation technique that combines inverse distance weighting with a triangulated irregular network with linear interpolation. State-of-the-art solutions for enhanced visualizations are included and essential metadata and paradata are also generated. In addition, we have introduced a QGIS plug-in that implements the pipeline as a one-step process. It reduces the manual workload by 75 to 90 percent and requires no special skills other than a general familiarity with the QGIS environment. It is intended that the pipeline and tool will contribute to the white-boxing of archaeology-specific airborne LiDAR data processing. In discussion, the role of data processing in the knowledge production process is explored.

scholarly journals 3D DATA PROCESSING TOWARD MAINTENANCE AND CONSERVATION. THE INTEGRATED DIGITAL DOCUMENTATION OF CASA DE VIDRO

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 65-72 ◽  
Author(s):  
M. Balzani ◽  
F. Maietti ◽  
L. Rossato
Keyword(s):  
Los Angeles ◽  
Point Cloud ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
3D Data ◽  
Architectural Features ◽  
3D Data Processing ◽  
Digital Documentation ◽  
Relationship Of ◽  
The Relationship

<p><strong>Abstract.</strong> During the last decade, 3D integrated surveys and BIM modelling procedures have greatly improved the overall knowledge on some Brazilian Modernist buildings. In this framework, the <i>Casa de Vidro</i> 3D survey carried out by DIAPReM centre at Ferrara University, beside the important outputs, analysis and researches achieved from the point cloud database processing, was also useful to test several awareness increasing activities in cooperation with local stakeholders.</p><p>The first digital documentation test of the Casa de Vidro allowed verifying the feasibility of a full survey on the building towards the restoration and possible placement of new architectures into the garden as an archive-museum of the Lina Bo and P.M. Bardi Foundation. Later, full 3D integrated survey and diagnostic analysis were carried out to achieve the total digital documentation of the house sponsored by the Keeping it Modern initiative of Getty Foundation (Los Angeles). Following its characteristics, the survey had to take into consideration the different architectural features, up to the relationship of architecture and nature. These 3D documentation activities and the point cloud processing allowed several analysis in a multidisciplinary framework.</p>

scholarly journals Point Cloud Processing via Recurrent Set Encoding

2019 ◽  
Vol 33 ◽  
pp. 5441-5449
Author(s):  
Pengxiang Wu ◽  
Chao Chen ◽  
Jingru Yi ◽  
Dimitris Metaxas
Keyword(s):  
Neural Network ◽  
Point Cloud ◽  
Feature Learning ◽  
Spatial Layout ◽  
Spatial Feature ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
The Arts ◽  
Point Set ◽  
Recurrent Set

We present a new permutation-invariant network for 3D point cloud processing. Our network is composed of a recurrent set encoder and a convolutional feature aggregator. Given an unordered point set, the encoder firstly partitions its ambient space into parallel beams. Points within each beam are then modeled as a sequence and encoded into subregional geometric features by a shared recurrent neural network (RNN). The spatial layout of the beams is regular, and this allows the beam features to be further fed into an efficient 2D convolutional neural network (CNN) for hierarchical feature aggregation. Our network is effective at spatial feature learning, and competes favorably with the state-of-the-arts (SOTAs) on a number of benchmarks. Meanwhile, it is significantly more efficient compared to the SOTAs.

scholarly journals Automatic Processing of Aerial LiDAR Data to Detect Vegetation Continuity in the Surroundings of Roads

2020 ◽  
Vol 12 (10) ◽  
pp. 1677 ◽  
Author(s):  
Ana Novo ◽  
Noelia Fariñas-Álvarez ◽  
Joaquin Martínez-Sánchez ◽  
Higinio González-Jorge ◽  
Henrique Lorenzo
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Applicable Law ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Preventive Actions ◽  
Total Study Area ◽  
Processing Techniques ◽  
Flammable Material

The optimization of forest management in the surroundings of roads is a necessary task in term of wildfire prevention and the mitigation of their effects. One of the reasons why a forest fire spreads is the presence of contiguous flammable material, both horizontally and vertically and, thus, vegetation management becomes essential in preventive actions. This work presents a methodology to detect the continuity of vegetation based on aerial Light Detection and Ranging (LiDAR) point clouds, in combination with point cloud processing techniques. Horizontal continuity is determined by calculating Cover Canopy Fraction (CCF). The results obtained show 50% of shrubs presence and 33% of trees presence in the selected case of study, with an error of 5.71%. Regarding vertical continuity, a forest structure composed of a single stratum represents 81% of the zone. In addition, the vegetation located in areas around the roads were mapped, taking into consideration the distances established in the applicable law. Analyses show that risky areas range from a total of 0.12 ha in a 2 m buffer and 0.48 ha in a 10 m buffer, representing a 2.4% and 9.5% of the total study area, respectively.

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