scholarly journals Point Cloud of Cloud Points (PCCP) Value-Added Product Report

2020 ◽  
Author(s):  
David Romps ◽  
Rusen Oktem
Keyword(s):  
Point Cloud ◽  
Value Added ◽  
Product Report ◽  
Cloud Points

scholarly journals Interactive Exploration of Large-Scale UI Datasets with Design Maps

2021 ◽  
Author(s):  
Luis A Leiva ◽  
Asutosh Hota ◽  
Antti Oulasvirta
Keyword(s):  
User Interface ◽  
Point Cloud ◽  
Large Scale ◽  
Mobile Apps ◽  
Online Resources ◽  
Design Tools ◽  
Support Design ◽  
Design Exploration ◽  
Clustering Techniques ◽  
Cloud Points

Abstract Designers are increasingly using online resources for inspiration. How to best support design exploration without compromising creativity? We introduce and study Design Maps, a class of point-cloud visualizations that makes large user interface datasets explorable. Design Maps are computed using dimensionality reduction and clustering techniques, which we analyze thoroughly in this paper. We present concepts for integrating Design Maps into design tools, including interactive visualization, local neighborhood exploration and functionality to integrate existing solutions to the design at hand. These concepts were implemented in a wireframing tool for mobile apps, which was evaluated with actual designers performing realistic tasks. Overall, designers find Design Maps supporting their creativity (avg. CSI score of 74/100) and indicate that the maps producing consistent whitespacing within cloud points are the most informative ones.

scholarly journals Oklahoma Mesonet Soil Moisture (OKMSOIL) Value-Added Product Report

2019 ◽  
Author(s):  
L Gregory ◽  
Alice Cialella ◽  
Scott Giangrande
Keyword(s):  
Soil Moisture ◽  
Value Added ◽  
Product Report ◽  
Oklahoma Mesonet

scholarly journals Aerosol Optical Depth Value-Added Product Report

2013 ◽  
Author(s):  
A Koontz ◽  
G Hodges ◽  
J Barnard ◽  
C Flynn ◽  
J Michalsky
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Value Added ◽  
Product Report

scholarly journals Accuracy assessment of building point clouds automatically generated from iphone images

2014 ◽  
Vol XL-5 ◽  
pp. 547-552 ◽  
Author(s):  
B. Sirmacek ◽  
R. Lindenbergh
Keyword(s):  
Measurement Errors ◽  
Point Cloud ◽  
Model Updating ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Point Clouds ◽  
3D Models ◽  
Map Updating ◽  
Cloud Points ◽  
Point To Point

Low-cost sensor generated 3D models can be useful for quick 3D urban model updating, yet the quality of the models is questionable. In this article, we evaluate the reliability of an automatic point cloud generation method using multi-view iPhone images or an iPhone video file as an input. We register such automatically generated point cloud on a TLS point cloud of the same object to discuss accuracy, advantages and limitations of the iPhone generated point clouds. For the chosen example showcase, we have classified 1.23% of the iPhone point cloud points as outliers, and calculated the mean of the point to point distances to the TLS point cloud as 0.11 m. Since a TLS point cloud might also include measurement errors and noise, we computed local noise values for the point clouds from both sources. Mean (μ) and standard deviation (&amp;sigma;) of roughness histograms are calculated as (μ<sub>1</sub> = 0.44 m., &amp;sigma;<sub>1</sub> = 0.071 m.) and (μ<sub>2</sub> = 0.025 m., &amp;sigma;<sub>2</sub> = 0.037 m.) for the iPhone and TLS point clouds respectively. Our experimental results indicate possible usage of the proposed automatic 3D model generation framework for 3D urban map updating, fusion and detail enhancing, quick and real-time change detection purposes. However, further insights should be obtained first on the circumstances that are needed to guarantee a successful point cloud generation from smartphone images.

scholarly journals Acquisition and Registration of Human Point Cloud Based on Depth Camera

2018 ◽  
Vol 173 ◽  
pp. 03001
Author(s):  
Huibai Wang ◽  
Shengyu Wang
Keyword(s):  
Point Cloud ◽  
Feature Matching ◽  
Least Square ◽  
Experimental Results ◽  
Depth Camera ◽  
Point Cloud Registration ◽  
Camera Pose ◽  
Cloud Points

Registration of human cloud points is a key step in 3D human reconstruction. So, this paper proposes using depth camera to acquire human point cloud. Then, using feature matching and PnP (Perspective-n-Point) to solve the camera pose.Finally, use least square to optimize camera pose and implement point cloud registration. Experimental results show the effectiveness of this method.

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