Geometry compression of 3-D mesh models using a joint prediction

2003 ◽  
Author(s):  
Jeong-Hwan Ahn ◽  
Yo-Sung Ho
Keyword(s):  
Geometry Compression ◽  
Joint Prediction ◽  
Mesh Models

Geometry compression of 3-D mesh models using predictive two-stage quantization

2000 ◽  
Vol 10 (2) ◽  
pp. 312-322 ◽  
Author(s):  
Jin Soo Choi ◽  
Yong Han Kim ◽  
Ho-Jang Lee ◽  
In-Sung Park ◽  
Myoung Ho Lee ◽  
...  
Keyword(s):  
Two Stage ◽  
Geometry Compression ◽  
Mesh Models

Spherical Partition Quantization Geometry Compression Algorithm of Three-Dimensional Mesh Models

2012 ◽  
Vol 5 (2) ◽  
pp. 823-826
Author(s):  
Lijun He ◽  
Yongkui Liu ◽  
Kejie Yuan ◽  
Jian Yun
Keyword(s):  
Three Dimensional ◽  
Compression Algorithm ◽  
Geometry Compression ◽  
Mesh Models

Variable Precision Depth Encoding for 3D Range Geometry Compression

2020 ◽  
Vol 2020 (17) ◽  
pp. 34-1-34-7
Author(s):  
Matthew G. Finley ◽  
Tyler Bell
Keyword(s):  
Normal Distribution ◽  
Arbitrary Distribution ◽  
File Size ◽  
Geometry Compression ◽  
Variable Precision ◽  
Wide Range ◽  
Novel Method ◽  
Encoding Method ◽  
Rgb Image ◽  
Color Channels

This paper presents a novel method for accurately encoding 3D range geometry within the color channels of a 2D RGB image that allows the encoding frequency—and therefore the encoding precision—to be uniquely determined for each coordinate. The proposed method can thus be used to balance between encoding precision and file size by encoding geometry along a normal distribution; encoding more precisely where the density of data is high and less precisely where the density is low. Alternative distributions may be followed to produce encodings optimized for specific applications. In general, the nature of the proposed encoding method is such that the precision of each point can be freely controlled or derived from an arbitrary distribution, ideally enabling this method for use within a wide range of applications.

Research on hole filling algorithm in triangle mesh models

2009 ◽  
Vol 29 (8) ◽  
pp. 2035-2037 ◽  
Author(s):  
Jian-lei TIAN ◽  
Xu-min LIU ◽  
Yong GUAN
Keyword(s):  
Triangle Mesh ◽  
Hole Filling ◽  
Mesh Models

scholarly journals Bootstrap joint prediction regions for sequences of missing values in spatio-temporal datasets

2021 ◽  
Author(s):  
Maria Lucia Parrella ◽  
Giuseppina Albano ◽  
Cira Perna ◽  
Michele La Rocca
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Sample Selection ◽  
Sample Path ◽  
Temporal Relationships ◽  
Empirical Performance ◽  
Spatio Temporal ◽  
Joint Prediction ◽  
Point Forecast ◽  
Prediction Regions

AbstractMissing data reconstruction is a critical step in the analysis and mining of spatio-temporal data. However, few studies comprehensively consider missing data patterns, sample selection and spatio-temporal relationships. To take into account the uncertainty in the point forecast, some prediction intervals may be of interest. In particular, for (possibly long) missing sequences of consecutive time points, joint prediction regions are desirable. In this paper we propose a bootstrap resampling scheme to construct joint prediction regions that approximately contain missing paths of a time components in a spatio-temporal framework, with global probability $$1-\alpha $$ 1 - α . In many applications, considering the coverage of the whole missing sample-path might appear too restrictive. To perceive more informative inference, we also derive smaller joint prediction regions that only contain all elements of missing paths up to a small number k of them with probability $$1-\alpha $$ 1 - α . A simulation experiment is performed to validate the empirical performance of the proposed joint bootstrap prediction and to compare it with some alternative procedures based on a simple nominal coverage correction, loosely inspired by the Bonferroni approach, which are expected to work well standard scenarios.

Three-dimensional range geometry compression via phase encoding

2017 ◽  
Vol 56 (33) ◽  
pp. 9285 ◽  
Author(s):  
Tyler Bell ◽  
Bogdan Vlahov ◽  
Jan P. Allebach ◽  
Song Zhang
Keyword(s):  
Three Dimensional ◽  
Phase Encoding ◽  
Geometry Compression ◽  
Dimensional Range

scholarly journals Modeling the pelvic region for non-invasive pelvic intraoperative neuromonitoring

2016 ◽  
Vol 2 (1) ◽  
pp. 185-188 ◽  
Author(s):  
Tomasz Moszkowski ◽  
Thilo Krüger ◽  
Werner Kneist ◽  
Klaus-Peter Hoffmann
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Intraoperative Neuromonitoring ◽  
Tetrahedral Mesh ◽  
Mesorectal Excision ◽  
Element Analysis ◽  
Pelvic Region ◽  
Non Invasive ◽  
Muscle Tissues ◽  
Mesh Models

AbstractFinite element analysis (FEA) of electric current distribution in the pelvis minor may help to assess the usability of non-invasive surface stimulation for continuous pelvic intraoperative neuromonitoring. FEA requires generation of quality volumetric tetrahedral mesh geometry. This study proposes the generation of a suitable mesh based on MRI data. The resulting volumetric mesh models the autonomous nerve structures at risk during total mesorectal excision. The model also contains the bone, cartilage, fat, skin, muscle tissues of the pelvic region, and a set of electrodes for surface stimulation. The model is ready for finite element analysis of the discrete Maxwell’s equations.

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