scholarly journals A New Method for Investigating Relationships Between Distribution of Sessile Organisms and Multiple Terrain Variables by Photogrammetry of Subtidal Bedrocks

2021 ◽  
Vol 8 ◽  
Author(s):  
Takayuki Kanki ◽  
Kenta Nakamoto ◽  
Jun Hayakawa ◽  
Takashi Kitagawa ◽  
Tomohiko Kawamura
Keyword(s):  
Point Cloud ◽  
New Method ◽  
Coralline Algae ◽  
Slope Aspect ◽  
Halocynthia Roretzi ◽  
High Position ◽  
Terrain Attributes ◽  
Cloud Models ◽  
Sessile Organisms ◽  
Mesh Models

Previous studies of habitat suitability of sessile organisms on subtidal rocky substrata have been focused only one or two terrain attributes. In this study, we propose a new method to construct a centimeter resolution seafloor topographic model by using underwater photogrammetry to obtain multiple terrain variables and to investigate relationships between the distribution of sessile organisms and multiple terrain variables. Point cloud models of five square sections (11.3–25.5 m2) of the bedrock surface of Otsuchi Bay were reconstructed with a 0.05 m resolution. Using the 0.01 m resolution point cloud models, five terrain variables were calculated on each face of the mesh models: height above seafloor, topological position index, slope, aspect, and ruggedness. The presence/absence data of four species of sessile organisms (ascidian Halocynthia roretzi, barnacle Balanus trigonus, polychaete Paradexiospira nakamurai, and articulated coralline algae Pachyarthron cretaceum) were located on the mesh models. H. roretzi and B. trigonus were more abundant on vertical and high faces above the seafloor, and P. nakamurai were more abundant at high faces above the surroundings. In high position where the current velocity increases, the three sessile animals may have an advantage for their suspension feeding. In contrast, P. cretaceum, unlike the other three sessile animal species, occurred at various heights and on gentle slope faces suitable for photosynthesis.

scholarly journals OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1563
Author(s):  
Ruibing Wu ◽  
Ziping Yu ◽  
Donghong Ding ◽  
Qinghua Lu ◽  
Zengxi Pan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Point Cloud ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Multiple Point ◽  
Icp Algorithm ◽  
Registration Algorithm ◽  
Cloud Models ◽  
Comparison Results ◽  
Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

scholarly journals The influence of subcolony-scale nesting habitat on the reproductive success of Adélie penguins

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Annie E. Schmidt ◽  
Grant Ballard ◽  
Amélie Lescroël ◽  
Katie M. Dugger ◽  
Dennis Jongsomjit ◽  
...  
Keyword(s):  
Reproductive Success ◽  
Colony Size ◽  
Nest Predation ◽  
Size Variation ◽  
Area Ratio ◽  
Slope Aspect ◽  
Nesting Habitat ◽  
Terrain Attributes ◽  
Small Colony ◽  
Adélie Penguins

AbstractGroup-size variation is common in colonially breeding species, including seabirds, whose breeding colonies can vary in size by several orders of magnitude. Seabirds are some of the most threatened marine taxa and understanding the drivers of colony size variation is more important than ever. Reproductive success is an important demographic parameter that can impact colony size, and it varies in association with a number of factors, including nesting habitat quality. Within colonies, seabirds often aggregate into distinct groups or subcolonies that may vary in quality. We used data from two colonies of Adélie penguins 73 km apart on Ross Island, Antarctica, one large and one small to investigate (1) How subcolony habitat characteristics influence reproductive success and (2) How these relationships differ at a small (Cape Royds) and large (Cape Crozier) colony with different terrain characteristics. Subcolonies were characterized using terrain attributes (elevation, slope aspect, slope steepness, wind shelter, flow accumulation), as well group characteristics (area/size, perimeter-to-area ratio, and proximity to nest predators). Reproductive success was higher and less variable at the larger colony while subcolony characteristics explained more of the variance in reproductive success at the small colony. The most important variable influencing subcolony quality at both colonies was perimeter-to-area ratio, likely reflecting the importance of nest predation by south polar skuas along subcolony edges. The small colony contained a higher proportion of edge nests thus higher potential impact from skua nest predation. Stochastic environmental events may facilitate smaller colonies becoming “trapped” by nest predation: a rapid decline in the number of breeding individuals may increase the proportion of edge nests, leading to higher relative nest predation and hindering population recovery. Several terrain covariates were retained in the final models but which variables, the shapes of the relationships, and importance varied between colonies.

A new method of point cloud boundary extraction based on slice

2021 ◽  
Author(s):  
Haipeng Zhu ◽  
Ming Huang ◽  
Chuanli Zhou
Keyword(s):  
Point Cloud ◽  
New Method ◽  
Boundary Extraction

scholarly journals VISUALIZATION OF POINT CLOUD MODELS IN MOBILE AUGMENTED REALITY USING CONTINUOUS LEVEL OF DETAIL METHOD

2020 ◽  
Vol XLIV-4/W1-2020 ◽  
pp. 167-170
Author(s):  
L. Zhang ◽  
P. van Oosterom ◽  
H. Liu
Keyword(s):  
Augmented Reality ◽  
Point Cloud ◽  
Cloud Model ◽  
Point Clouds ◽  
Level Of Detail ◽  
Mobile Augmented Reality ◽  
Cloud Models ◽  
Point Cloud Model ◽  
Visual Artifacts ◽  
Mobile Ar

Abstract. Point clouds have become one of the most popular sources of data in geospatial fields due to their availability and flexibility. However, because of the large amount of data and the limited resources of mobile devices, the use of point clouds in mobile Augmented Reality applications is still quite limited. Many current mobile AR applications of point clouds lack fluent interactions with users. In our paper, a cLoD (continuous level-of-detail) method is introduced to filter the number of points to be rendered considerably, together with an adaptive point size rendering strategy, thus improve the rendering performance and remove visual artifacts of mobile AR point cloud applications. Our method uses a cLoD model that has an ideal distribution over LoDs, with which can remove unnecessary points without sudden changes in density as present in the commonly used discrete level-of-detail approaches. Besides, camera position, orientation and distance from the camera to point cloud model is taken into consideration as well. With our method, good interactive visualization of point clouds can be realized in the mobile AR environment, with both nice visual quality and proper resource consumption.

scholarly journals Point-Cloud Models of Historical Barns – Spatial Discrepancies of Laser Scanning versus Robotic Total Station

10.29007/2493 ◽  
2020 ◽  
Author(s):  
Gustavo Maldonado ◽  
Marcel Maghiar ◽  
Brent Tharp ◽  
Dhruv Patel
Keyword(s):  
Service Learning ◽  
Point Cloud ◽  
Laser Scanning ◽  
3D Point Cloud ◽  
Spatial Accuracy ◽  
Total Station ◽  
Cloud Models ◽  
Bulloch County ◽  
Southern University ◽  
Robotic Total Station

This study considers the generation of virtual, 3D point-cloud models of seven deteriorating historical, agricultural barns in Bulloch County, Georgia, USA, for preservation purposes. The work was completed as a service-learning project in a course on Terrestrial Light Detection and Ranging (T-LiDAR), offered at Georgia Southern University. The resulting models and fly-through videos were donated to Bulloch County Historical Society and to the Georgia Southern Museum, to make them available to the general public and future generations. Additionally, one of the seven barns was selected to be extensively measured to estimate the relative spatial accuracy of all seven resulting 3D point-cloud models, with respect to measurements completed with a highly accurate instrument. Three accurate benchmarks were established around it for georeferencing purposes. The positions of 44 points were measured in the field via an accurate, one- second, robotic total-station (RTS) instrument. Also, the coordinates of the same points were acquired from within georeferenced and non-georeferenced point-cloud models. These points defined 259 distances. They were compared to determine their discrepancy statistics. It was observed that this process produced virtual models with an approximate maximum spatial discrepancy of one-half inch (0.5 in) with respect to measurements performed by a highly accurate RTS device. There were no substantial differences in the relative accuracies of the georeferenced and non-georeferenced models.

scholarly journals MXenes as Flow Electrodes for Capacitive Deionization of Wastewater

2020 ◽  
Author(s):  
Kathryn Primerose Drake
Keyword(s):  
Power Spectrum ◽  
Point Cloud ◽  
Vector Fields ◽  
Big Bang ◽  
New Method ◽  
Fundamental Result ◽  
Irregular Domains ◽  
Angular Power Spectrum ◽  
Vector Calculus ◽  
Normal Vectors

This dissertation addresses problems that arise in a diverse group of fields including cosmology, electromagnetism, and graphic design. While these topics may seem disparate, they share a commonality in their need for fast and accurate algorithms which can handle large datasets collected on irregular domains. An important issue in cosmology is the calculation of the angular power spectrum of the cosmic microwave background (CMB) radiation. CMB photons offer a direct insight into the early stages of the universe's development and give the strongest evidence for the Big Bang theory to date. The Hierarchical Equal Area isoLatitude Pixelation (HEALPix) grid is used by cosmologists to collect CMB data and store it as points on the sphere. HEALPix also refers to the software package that analyzes CMB maps and calculates their angular power spectrums. Refined analysis of the CMB angular power spectrum can lead to revolutionary developments in understanding the curvature of the universe, dark matter density, and the nature of dark energy. In the first paper, we present a new method for performing spherical harmonic analysis for HEALPix data, which is a vital component for computing the CMB angular power spectrum. Using numerical experiments, we demonstrate that the new method provides better accuracy and a higher convergence rate when compared to the current methods on synthetic data. This paper is presented in Chapter 2. The problem of constructing smooth approximants to divergence-free (div-free) and curl-free vector fields and/or their potentials based only on discrete samples arises in science applications like fluid dynamics and electromagnetism. It is often necessary that the vector approximants preserve the div-free or curl-free properties of the field. Div/curl-free radial basis functions (RBFs) have traditionally been utilized for constructing these vector approximants, but their global nature can make them computationally expensive and impractical. In the second paper, we develop a technique for bypassing this issue that combines div/curl-free RBFs in a partition of unity (PUM) framework, where one solves for local approximants over subsets of the global samples and then blends them together to form a div-free or curl-free global approximant. This method can be used to approximate vector fields and their scalar potentials on the sphere and in irregular domains in ℝ2 and ℝ3. We present error estimates and demonstrate the effectiveness of the method on several test problems. This paper is presented in Chapter 3. The issue of reconstructing implicit surfaces from oriented point clouds has applications in computer aided design, medical imaging, and remote sensing. Utilizing the technique from the second paper, we introduce a novel approach to this problem by exploiting a fundamental result from vector calculus. In our method, deemed CFPU, we interpolate the normal vectors of the point cloud with a curl-free RBF-PUM interpolant and extract a potential of the reconstructed vector field. The zero-level surface of this potential approximates the implicit surface of the point cloud. Benefits of this method include its ability to represent local sharp features, handle noise in the normal vectors, and even exactly interpolate a point cloud. We demonstrate in the third paper that our method converges for known surfaces and also show how it performs on various surfaces found in the literature. This paper is presented in Chapter 4.

scholarly journals Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data

2016 ◽  
Vol 10 (1) ◽  
pp. 257-269 ◽  
Author(s):  
Z. Zheng ◽  
P. B. Kirchner ◽  
R. C. Bales
Keyword(s):  
Sierra Nevada ◽  
Snow Depth ◽  
Point Cloud ◽  
Accumulation Point ◽  
Snow Accumulation ◽  
Slope Aspect ◽  
Conifer Forest ◽  
Vegetation Effects ◽  
Cloud Data ◽  
Depth Measurement

Abstract. Airborne light detection and ranging (lidar) measurements carried out in the southern Sierra Nevada in 2010 in the snow-free and peak-snow-accumulation periods were analyzed for topographic and vegetation effects on snow accumulation. Point-cloud data were processed from four primarily mixed-conifer forest sites covering the main snow-accumulation zone, with a total surveyed area of over 106 km2. The percentage of pixels with at least one snow-depth measurement was observed to increase from 65–90 to 99 % as the sampling resolution of the lidar point cloud was increased from 1 to 5 m. However, a coarser resolution risks undersampling the under-canopy snow relative to snow in open areas and was estimated to result in at least a 10 cm overestimate of snow depth over the main snow-accumulation region between 2000 and 3000 m, where 28 % of the area had no measurements. Analysis of the 1 m gridded data showed consistent patterns across the four sites, dominated by orographic effects on precipitation. Elevation explained 43 % of snow-depth variability, with slope, aspect and canopy penetration fraction explaining another 14 % over the elevation range of 1500–3300 m. The relative importance of the four variables varied with elevation and canopy cover, but all were statistically significant over the area studied. The difference between mean snow depth in open versus under-canopy areas increased with elevation in the rain–snow transition zone (1500–1800 m) and was about 35 ± 10 cm above 1800 m. Lidar has the potential to transform estimation of snow depth across mountain basins, and including local canopy effects is both feasible and important for accurate assessments.

An Automatic Designing Method for Apparel

2011 ◽  
Vol 88-89 ◽  
pp. 175-179
Author(s):  
Xiao Gang Wang ◽  
Qin Zheng ◽  
Xin Zhan Li
Keyword(s):  
Point Cloud ◽  
Science Research ◽  
Mathematic Model ◽  
New Method ◽  
Scanning System ◽  
Body Scanning ◽  
3D Shape ◽  
Vertical Sections ◽  
Set Up ◽  
Designing Method

In this article we discuss a new method for describing the 3D shape of woman warm jacket and set up its mathematic model, which is by dint of body scanning technology. Telmat scanning system scanned samples. The scanning point cloud were analyzed in horizontal and vertical sections. Outlines of vertical sections were described and mathematic models were set up. The result helped to prognosticate the shape of woman warm jacket. A new describing method for 3D shape is discussed. And it opens our mind to utilize body-scanning technology for deeper science research.

Sign in / Sign up

Export Citation Format

Share Document