Continuous-Scale 3D Terrain Visualization Based on a Detail-Increment Model

Ai;  Wang;  Yang;  Bu;  Lin;  Lv

doi:10.3390/ijgi8100465

Continuous-Scale 3D Terrain Visualization Based on a Detail-Increment Model

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8100465 ◽

2019 ◽

Vol 8 (10) ◽

pp. 465 ◽

Cited By ~ 2

Author(s):

Ai ◽

Wang ◽

Yang ◽

Bu ◽

Lin ◽

...

Keyword(s):

Simplified Model ◽

Terrain Rendering ◽

Data Redundancy ◽

Terrain Visualization ◽

Terrain Model ◽

Scale Transition ◽

Model Version ◽

Continuous Scale ◽

Incremental Information ◽

Edge Collapse

Triangulated irregular networks (TINs) are widely used in terrain visualization due to their accuracy and efficiency. However, the conventional algorithm for multi-scale terrain rendering, based on TIN, has many problems, such as data redundancy and discontinuities in scale transition. To solve these issues, a method based on a detail-increment model for the construction of a continuous-scale hierarchical terrain model is proposed. First, using the algorithm of edge collapse, based on a quadric error metric (QEM), a complex terrain base model is processed to a most simplified model version. Edge collapse records at different scales are stored as compressed incremental information in order to make the rendering as simple as possible. Then, the detail-increment hierarchical terrain model is built using the incremental information and the most simplified model version. Finally, the square root of the mean minimum quadric error (MMQE), calculated by the points at each scale, is considered the smallest visible object (SVO) threshold that allows for the scale transition with the required scale or the visual range. A point cloud from Yanzhi island is converted into a hierarchical TIN model to verify the effectiveness of the proposed method. The results show that the method has low data redundancy, and no error existed in the topology. It can therefore meet the basic requirements of hierarchical visualization.

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The Research of 3D Terrain Visualization Based on ArcGIS and 3DMAX

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.90-93.3287 ◽

2011 ◽

Vol 90-93 ◽

pp. 3287-3291

Author(s):

Mao Yi Tian ◽

Xing Wang ◽

Qian Zhao

Keyword(s):

High Precision ◽

3D Model ◽

Discrete Data ◽

Data Type ◽

Terrain Modeling ◽

Terrain Visualization ◽

Terrain Model ◽

3D Terrain ◽

Characteristic Points

The paper researches how to import the large amount of discrete data into 3DMAX and realizes high precision 3D terrain modeling. By analyzing and researching the data type and the feature of discrete data which can be accepted in 3DMAX, this author proposes a technical by importing data to ArcGIS, adding and modifying the characteristic points and lines, smoothing the data, exporting the 3D model in the use of the ArcGIS, and finally importing the result model into 3DMAX, and then generating the high precision 3D terrain model. In practice, the feasibility of this process is proved very well.

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A Multilevel Terrain Rendering Method Based on Dynamic Stitching Strips

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8060255 ◽

2019 ◽

Vol 8 (6) ◽

pp. 255

Author(s):

Liwei Zhang ◽

Jiangfeng She ◽

Junzhong Tan ◽

Biao Wang ◽

Yuchang Sun

Keyword(s):

Real Time ◽

Graphics Processing Unit ◽

Processing Unit ◽

High Quality ◽

Terrain Rendering ◽

Terrain Model ◽

Terrain Models ◽

Terrain Representation ◽

Graphics Processing ◽

Different Levels

High-quality terrain rendering has been the focus of many visualization applications over recent decades. Many terrain rendering methods use the strategy of Level of Detail (LOD) to create adaptive terrain models, but the transition between different levels is usually not handled well, which may cause popping artefacts that seriously affect the reality of the terrain model. In recent years, many researchers have tried using modern Graphics Processing Unit (GPU) to complete heavy rendering tasks. By leveraging the great power of GPU, high quality terrain models with rich details can be rendered in real time, although the problem of popping artefacts still persists. In this study, we propose a real-time terrain rendering method with GPU tessellation that can effectively reduce the popping artefacts. Coupled with a view-dependent updating scheme, a multilevel terrain representation based on the flexible Dynamic Stitching Strip (DSS) is developed. During rendering, the main part of the terrain model is tessellated into appropriate levels using GPU tessellation. DSSs, generated in parallel, can seamlessly make the terrain transitions between different levels much smoother. Experiments demonstrate that the proposed method can meet the requirements of real-time rendering and achieve a better visual quality compared with other methods.

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Scale transition models and X-ray stress analysis

Revue de Métallurgie ◽

10.1051/metal:2003182 ◽

2003 ◽

Vol 100 (12) ◽

pp. 1137-1149

Author(s):

M. François

Keyword(s):

Stress Analysis ◽

X Ray ◽

Scale Transition ◽

Transition Models

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A simplified model for the aerodynamic analysis of a small propeller

10.26678/abcm.cobem2017.cob17-2581 ◽

2017 ◽

Author(s):

Kamal Ismail ◽

Célia Rosolen

Keyword(s):

Simplified Model ◽

Aerodynamic Analysis

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The nature of metacognitive inefficiency in perceptual decision making

10.31234/osf.io/g9qzh ◽

2020 ◽

Author(s):

Medha Shekhar ◽

Dobromir Rahnev

Keyword(s):

Decision Making ◽

Mechanistic Model ◽

Perceptual Decision Making ◽

Large Dataset ◽

Substantial Body ◽

Confidence Levels ◽

Continuous Scale ◽

Competing Models ◽

Metacognitive Ability ◽

Confidence Ratings

Humans have the metacognitive ability to judge the accuracy of their own decisions via confidence ratings. A substantial body of research has demonstrated that human metacognition is fallible but it remains unclear how metacognitive inefficiency should be incorporated into a mechanistic model of confidence generation. Here we show that, contrary to what is typically assumed, metacognitive inefficiency depends on the level of confidence. We found that, across five different datasets and four different measures of metacognition, metacognitive ability decreased with higher confidence ratings. To understand the nature of this effect, we collected a large dataset of 20 subjects completing 2,800 trials each and providing confidence ratings on a continuous scale. The results demonstrated a robustly nonlinear zROC curve with downward curvature, despite a decades-old assumption of linearity. This pattern of results was reproduced by a new mechanistic model of confidence generation, which assumes the existence of lognormally-distributed metacognitive noise. The model outperformed competing models either lacking metacognitive noise altogether or featuring Gaussian metacognitive noise. Further, the model could generate a measure of metacognitive ability which was independent of confidence levels. These findings establish an empirically-validated model of confidence generation, have significant implications about measures of metacognitive ability, and begin to reveal the underlying nature of metacognitive inefficiency.

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