An Improved Cloth Simulation Filtering Algorithm Based on Mining Point Cloud

Ren, Liangcai; Tang, Jun; Cui, Chenglin; Song, Ruiqi; Ai, Yunfeng

doi:10.1109/iccsi53130.2021.9736201

Cited by 4 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the corresponding weight values of convolution layers are obtained through the network training process. Considering the fact that the local features are too small to be observed [7], the simulated annealing algorithm is utilized to boost its performance. Moreover, the local gradient descent method also contributes to the improvemrnt of its efficiency and effectiveness.…”

Section: Figure 3: Schematic Diagram Of Data Segmentation Grid Using ...mentioning

confidence: 99%

Research on Point Cloud Filtering Data Processing Method Based on Self-adaptive Euclidean Clustering Network

2022

AJCIS

View full text Add to dashboard Cite

Nowadays, with the development of 3D filtering information processing by data algorithms, people have deeply studied 3D data processing. A series of issues were found in the research process. Moreover, the current research on point cloud data in the use of lidar is not comprehensive. Its research focuses on noise reduction and data block segmentation of point cloud-filtered data. Based on the above background, this paper analyzes the bilateral characteristics of point cloud filtering data in detail. It improves the adaptive clustering network to cluster the sample center points of the point cloud filtering data, effectively improving its readability and processing characteristics.

show abstract

Section: Figure 3: Schematic Diagram Of Data Segmentation Grid Using ...mentioning

confidence: 99%

Research on Point Cloud Filtering Data Processing Method Based on Self-adaptive Euclidean Clustering Network

2022

AJCIS

View full text Add to dashboard Cite

show abstract

“…Hence, they are more effective in flat areas but less so in areas with drastic terrain changes. The goal of surfacebased filtering [45][46][47] is to approximate bare-earth points using a mathematical description of a Digital Terrain Model (DTM); however, it tends to ignore terrain details. Given the characteristics of UAV LiDAR data in archaeological sites (e.g., steep areas with land features of different sizes), surface-based filtering will likely be most suitable for this study.…”

mentioning

confidence: 99%

UAV-Based Remote Sensing for Detection and Visualization of Partially-Exposed Underground Structures in Complex Archaeological Sites

Shin

Rastiveis

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The utilization of remote sensing technologies for archaeology was motivated by their ability to map large areas within a short time at a reasonable cost. With recent advances in platform and sensing technologies, uncrewed aerial vehicles (UAV) equipped with imaging and Light Detection and Ranging (LiDAR) systems have emerged as a promising tool due to their low cost, ease of deployment/operation, and ability to provide high-resolution geospatial data. In some cases, archaeological sites might be covered with vegetation, which makes the identification of below-canopy structures quite challenging. The ability of LiDAR energy to travel through gaps within vegetation allows for the derivation of returns from hidden structures below the canopy. This study deals with the development and deployment of a UAV system equipped with imaging and LiDAR sensing technologies assisted by an integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) for the archaeological mapping of Dana Island, Turkey. Data processing strategies are also introduced for the detection and visualization of underground structures. More specifically, a strategy has been developed for the robust identification of ground/terrain surface in a site characterized by steep slopes and dense vegetation, as well as the presence of numerous underground structures. The derived terrain surface is then used for the automated detection/localization of underground structures, which are then visualized through a web portal. The proposed strategy has shown a promising detection ability with an F1-score of approximately 92%.

show abstract

Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform

Wang

Fan

et al. 2023

Plant Phenomics

View full text Add to dashboard Cite

The field phenotyping platforms that can obtain high-throughput and time-series phenotypes of plant populations at the 3-dimensional level are crucial for plant breeding and management. However, it is difficult to align the point cloud data and extract accurate phenotypic traits of plant populations. In this study, high-throughput, time-series raw data of field maize populations were collected using a field rail-based phenotyping platform with light detection and ranging (LiDAR) and an RGB (red, green, and blue) camera. The orthorectified images and LiDAR point clouds were aligned via the direct linear transformation algorithm. On this basis, time-series point clouds were further registered by the time-series image guidance. The cloth simulation filter algorithm was then used to remove the ground points. Individual plants and plant organs were segmented from maize population by fast displacement and region growth algorithms. The plant heights of 13 maize cultivars obtained using the multi-source fusion data were highly correlated with the manual measurements ( R 2 = 0.98), and the accuracy was higher than only using one source point cloud data ( R 2 = 0.93). It demonstrates that multi-source data fusion can effectively improve the accuracy of time series phenotype extraction, and rail-based field phenotyping platforms can be a practical tool for plant growth dynamic observation of phenotypes in individual plant and organ scales.

show abstract

An Improved Cloth Simulation Filtering Algorithm Based on Mining Point Cloud

Cited by 4 publications

References 0 publications

Research on Point Cloud Filtering Data Processing Method Based on Self-adaptive Euclidean Clustering Network

Research on Point Cloud Filtering Data Processing Method Based on Self-adaptive Euclidean Clustering Network

UAV-Based Remote Sensing for Detection and Visualization of Partially-Exposed Underground Structures in Complex Archaeological Sites

Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform

Contact Info

Product

Resources

About