scholarly journals CONSISTENT MULTI-VIEW TEXTURING OF DETAILED 3D SURFACE MODELS

2015 ◽  
Vol II-3/W4 ◽  
pp. 25-31 ◽  
Author(s):  
K. Davydova ◽  
G. Kuschk ◽  
L. Hoegner ◽  
P. Reinartz ◽  
U. Stilla
Keyword(s):  
Large Scale ◽  
Texture Mapping ◽  
Aerial Images ◽  
Field Energy ◽  
Multiple Camera ◽  
3D Surface ◽  
Surface Models ◽  
Mapping Techniques ◽  
3D Scene ◽  
High Degree

Texture mapping techniques are used to achieve a high degree of realism for computer generated large-scale and detailed 3D surface models by extracting the texture information from photographic images and applying it to the object surfaces. Due to the fact that a single image cannot capture all parts of the scene, a number of images should be taken. However, texturing the object surfaces from several images can lead to lighting variations between the neighboring texture fragments. In this paper we describe the creation of a textured 3D scene from overlapping aerial images using a Markov Random Field energy minimization framework. We aim to maximize the quality of the generated texture mosaic, preserving the resolution from the original images, and at the same time to minimize the seam visibilities between adjacent fragments. As input data we use a triangulated mesh of the city center of Munich and multiple camera views of the scene from different directions.

scholarly journals LARGE SCALE CITY MAPPING USING SATELLITE IMAGERY / KOSMINIŲ NUOTRAUKŲ IŠ GOOGLE EARTH, TAIKOMŲ MIESTAMS KARTOGRAFUOTI STAMBIUOJU MASTELIU, REKTIFIKAVIMAS / РЕКТИФИКАЦИЯ КОСМИЧЕСКИХ СНИМКОВ ИЗ GOOGLE EARTH ДЛЯ КРУПНОМАСШТАБНОГО КАРТОГРАФИРОВАНИЯ ГОРОДОВ

2012 ◽  
Vol 37 (4) ◽  
pp. 168-171 ◽  
Author(s):  
Birutė Ruzgienė ◽  
Qian Yi Xiang ◽  
Silvija Gečytė
Keyword(s):  
Satellite Imagery ◽  
Large Scale ◽  
Modern Technology ◽  
Google Earth ◽  
Aerial Images ◽  
Control Points ◽  
Image Rectification ◽  
Image Deformation ◽  
Map Construction ◽  
Short Period

The rectification of high resolution digital aerial images or satellite imagery employed for large scale city mapping is modern technology that needs well distributed and accurately defined control points. Digital satellite imagery, obtained using widely known software Google Earth, can be applied for accurate city map construction. The method of five control points is suggested for imagery rectification introducing the algorithm offered by Prof. Ruan Wei (tong ji University, Shanghai). Image rectification software created on the basis of the above suggested algorithm can correct image deformation with required accuracy, is reliable and keeps advantages in flexibility. Experimental research on testing the applied technology has been executed using GeoEye imagery with Google Earth builder over the city of Vilnius. Orthophoto maps at the scales of 1:1000 and 1:500 are generated referring to the methodology of five control points. Reference data and rectification results are checked comparing with those received from processing digital aerial images using a digital photogrammetry approach. The image rectification process applying the investigated method takes a short period of time (about 4-5 minutes) and uses only five control points. The accuracy of the created models satisfies requirements for large scale mapping. Santrauka Didelės skiriamosios gebos skaitmeninių nuotraukų ir kosminių nuotraukų rektifikavimas miestams kartografuoti stambiuoju masteliu yra nauja technologija. Tai atliekant būtini tikslūs ir aiškiai matomi kontroliniai taškai. Skaitmeninės kosminės nuotraukos, gautos taikant plačiai žinomą programinį paketą Google Earth, gali būti naudojamos miestams kartografuoti dideliu tikslumu. Siūloma nuotraukas rektifikuoti Penkių kontrolinių taskų metodu pagal prof. Ruan Wei (Tong Ji universitetas, Šanchajus) algoritmą. Moksliniam eksperimentui pasirinkta Vilniaus GeoEye nuotrauka iš Google Earth. 1:1000 ir 1:500 mastelio ortofotografiniai žemėlapiai sudaromi Penkių kontrolinių taškų metodu. Rektifikavimo duomenys lyginami su skaitmeninių nuotraukų apdorojimo rezultatais, gautais skaitmeninės fotogrametrijos metodu. Nuotraukų rektifikavimas Penkių kontrolinių taskų metodu atitinka kartografavimo stambiuoju masteliu reikalavimus, sumažėja laiko sąnaudos. Резюме Ректификация цифровых и космических снимков высокой резолюции для крупномасштабного картографирования является новой технологией, требующей точных и четких контрольных точек. Цифровые космические снимки, полученные с использованием широкоизвестного программного пакета Google Earth, могут применяться для точного картографирования городов. Для ректификации снимков предложен метод пяти контрольных точек с применением алгоритма проф. Ruan Wei (Университет Tong Ji, Шанхай). Для научного эксперимента использован снимок города Вильнюса GeoEye из Google Earth. Ортофотографические карты в масштабе 1:1000 и 1:500 генерируются с применением метода пяти контрольных точек. Полученные результаты и данные ректификации сравниваются с результатами цифровых снимков, полученных с применением метода цифровой фотограмметрии. Ректификация снимков с применением метода пяти контрольных точек уменьшает временные расходы и удовлетворяет требования, предъявляемые к крупномасштабному картографированию.

scholarly journals Spatial multiplexing holographic combiner for glasses-free augmented reality

Nanophotonics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 3003-3010
Author(s):  
Jiacheng Shi ◽  
Wen Qiao ◽  
Jianyu Hua ◽  
Ruibin Li ◽  
Linsen Chen
Keyword(s):  
Augmented Reality ◽  
Large Scale ◽  
Motion Parallax ◽  
Visible Spectrum ◽  
Physical World ◽  
Spatial Multiplexing ◽  
White Balance ◽  
Full Color ◽  
Entire Visible Spectrum ◽  
3D Scene

AbstractGlasses-free augmented reality is of great interest by fusing virtual 3D images naturally with physical world without the aid of any wearable equipment. Here we propose a large-scale spatial multiplexing holographic see-through combiner for full-color 3D display. The pixelated metagratings with varied orientation and spatial frequency discretely reconstruct the propagating lightfield. The irradiance pattern of each view is tailored to form super Gaussian distribution with minimized crosstalk. What’s more, spatial multiplexing holographic combiner with customized aperture size is adopted for the white balance of virtually displayed full-color 3D scene. In a 32-inch prototype, 16 views form a smooth parallax with a viewing angle of 47°. A high transmission (>75%) over the entire visible spectrum range is achieved. We demonstrated that the displayed virtual 3D scene not only preserved natural motion parallax, but also mixed well with the natural objects. The potential applications of this study include education, communication, product design, advertisement, and head-up display.

scholarly journals Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information

2021 ◽  
Vol 13 (3) ◽  
pp. 364
Author(s):  
Han Gao ◽  
Jinhui Guo ◽  
Peng Guo ◽  
Xiuwan Chen
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Spatial Resolution ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Training Data ◽  
Aerial Images ◽  
Rural Landscapes ◽  
Feature Representations ◽  
Object Based

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha).

The academic profession in Russia’s two capitals: The impact of 20 years of transition

2017 ◽  
Vol 16 (5) ◽  
pp. 626-644 ◽  
Author(s):  
Elizaveta Sivak ◽  
Maria Yudkevich
Keyword(s):  
Large Scale ◽  
Academic Career ◽  
Academic Profession ◽  
University Faculty ◽  
Key Characteristics ◽  
Personal Strain ◽  
High Degree ◽  
The Impact ◽  
Russian Universities

This paper studies the dynamics of key characteristics of the academic profession in Russia based on the analysis of university faculty in the two largest cities in Russia – Moscow and St Petersburg. We use data on Russian university faculty from two large-scale comparative studies of the academic profession (‘The Carnegie Study’ carried out in 1992 in 14 countries, including Russia, and ‘The Changing Academic Profession Study’, 2007–2012, with 19 participating countries and which Russia joined in 2012) to look at how faculty’s characteristics and attitudes toward different aspects of their academic life changed over 20 years (1992–2011) such as faculty’s views on reasons to leave or to stay at a university, on university’s management and the role of faculty in decision making. Using the example of universities in the two largest Russian cities, we demonstrate that the high degree of overall centralization of governance in Russian universities barely changed in 20 years. Our paper provides comparisons of teaching/research preferences and views on statements concerning personal strain associated with work, academic career perspectives, etc., not only in Russian universities between the years 1992 and 2012, but also in Russia and other ‘Changing Academic Profession’ countries.

Research on Moving Target Monitoring Collaborative Simulation Technology Based on Multi-Channel

2010 ◽  
Vol 20-23 ◽  
pp. 700-705
Author(s):  
Tian Yuan ◽  
Shang Guan Wei ◽  
Zhi Zhong Lu
Keyword(s):  
Large Scale ◽  
Target Space ◽  
Laboratory Simulation ◽  
Moving Target ◽  
Simulation Technology ◽  
Collaborative Simulation ◽  
Mobile Target ◽  
Target Monitoring ◽  
Six Degree Of Freedom ◽  
High Degree

Multi-channel Virtual reality simulation technology is a kind of simulation technology, which support the grand scene and high degree of immersion, has better visualization effect. In this paper, a moving target monitoring collaboratory simulation technology based on multi-channel is studied. Firstly, study the mathematical modeling foundation of Multi-Channel technology systematically, based on the mobile target spatial model and co-simulation technology, select the appropriate applications of multi-channel technology, building laboratory simulation platform and achieved a space-based six-degree of freedom simulation of multi-channel moving target monitoring simulation. The experiment has proved that in multi-channel target monitoring co-simulation technology used in this paper has strong practicality, combine with a moving target-space model and co-simulation technology, the advantages of objective observation to solve the requirements like large-scale, realism, immersion requirements, etc.

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