Rapid and Automatic Reconstruction of Large-scale Areas
Rapid and Automatic Reconstruction of Large-scale Areas
批准号:
RGPIN-2016-06689
负责人:
Poullis, Charalambos
金额:
$1.6万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2018
资助国家:
加拿大
项目状态:
已结题
起止时间:
2018-01-01 至 2019-12-31
中文摘要
近年来,人们对现实虚拟世界的需求越来越大,虚拟世界代表了大规模的现实世界区域,包括地形、建筑、树木、汽车等。已经报道了许多采用这种逼真的虚拟表示的成功应用,包括用于培训应急人员的高级灾害管理模拟、用于城市规划和发展的施工前就地可视化新结构,以及故事情节发生在真实地点而不是虚构地点的电脑游戏。举个例子,育碧的《刺客信条》包含了许多文化遗产(尽管有些保存得并不完美),如圣索菲亚大教堂、加拉塔、凯里尼亚和利马索尔的城堡等******然而,尽管在这一领域做了大量的工作,但仍然存在许多挑战。目前,创建逼真的大规模3D内容仍然是一项复杂、耗时、昂贵和劳动密集型的任务。事实上,模型的创建仍然被广泛地视为一门专门的艺术,需要具有广泛培训和经验的人员来制作有用的模型。为了弥合目前最先进的技术与快速和自动创建大规模区域的最终目标之间的差距,基础研究是必不可少的。****该提案解决了当前大规模区域快速自动重建的技术难题,并寻求开发准确,稳健和可扩展的方法和系统的解决方案,用于处理主动式和被动式传感器捕获的大数据,以产生逼真的虚拟表示。更具体地说,该研究计划提出进一步研究和开发新颖而强大的算法,以准确地检测和提取:***(a)从被动和主动遥感器(即航空/卫星图像和激光雷达)捕获的数据中获取结构信息,并重建代表获取区域的地形、建筑物、汽车和树木模型的几何形状;***(b)从地面、斜空中和卫星传感器捕获的图像中获取外观信息。并将这些信息融合到3D模型的真实复合纹理地图集中。******本研究计划预计将对解决与现实虚拟世界创造领域高度实际相关的复杂问题做出实质性贡献。它还将有助于计算机视觉、计算机图形学和计算机游戏等一般领域的创新方法的发展
英文摘要
In recent years there has been an increasing demand for realistic virtual worlds representing large-scale, real-world areas comprising of terrain, buildings, trees, cars, etc. Many successful applications employing such realistic virtual representations have already been reported including advanced disaster management simulations for the training of emergency response personnel, visualizing new structures in-situ prior to construction for urban planning and development, and computer games where the storyline takes place in an actual rather than fictional location. For example Ubisoft's "Assassin's Creed" contains numerous cultural heritage sites [albeit some of them not in perfect condition], such as the Hagia Sophia, the Galata tower, the castles in Kyrenia and Limassol, etc.******However, despite the large volume of work in the area many challenges still remain. Currently, the creation of realistic large-scale 3D content remains a complex, time-consuming, expensive and labor-intensive task. In fact, the creation of models is still widely viewed as a specialized art, requiring personnel with extensive training and experience to produce useful models. Fundamental research is essential in order to bridge the gap between the current state-of-the-art and the ultimate goal of rapid and automatic creation of large-scale areas.****This proposal addresses the current technological difficulties of rapid and automatic reconstruction of large scale areas and seeks solutions for the development of accurate, robust and scalable methods and systems for processing the big data captured by active and passive sensors in order to produce a realistic virtual representation. More specifically the research program proposes further study and development of novel and robust algorithms for accurately detecting and extracting:***(a) structural information from data captured from passive and active remote sensors i.e. aerial/satellite images and LiDAR, and reconstructing the geometry of the terrain, buildings, cars and tree models representing the acquired area,***(b) appearance information from imagery captured from ground, oblique-aerial and satellite sensors, and fusing this information into realistic composite texture atlases of the 3D models.******This research program is expected to make substantial contributions to the solution of complex problems of high practical relevance to the field of realistic virtual world creation. It will also contribute to the development of innovative methods in the general fields of computer vision, computer graphics and computer games.**
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Semantic Segmentation in Geospatial Computer Vision
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批准号:RGPIN-2021-03479
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项目类别:Discovery Grants Program - Individual
-
资助金额:$2.11万
-
财政年份:2022
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负责人:Poullis, Charalambos
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依托单位:
ACESO: Computer Vision Algorithms for Computer-Assisted Surgical Systems
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批准号:567101-2021
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项目类别:Alliance Grants
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资助金额:$2.91万
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财政年份:2021
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负责人:Poullis, Charalambos
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依托单位:
Semantic Segmentation in Geospatial Computer Vision
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批准号:RGPIN-2021-03479
-
项目类别:Discovery Grants Program - Individual
-
资助金额:$2.11万
-
财政年份:2021
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负责人:Poullis, Charalambos
-
依托单位:
Rapid and Automatic Reconstruction of Large-scale Areas
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批准号:RGPIN-2016-06689
-
项目类别:Discovery Grants Program - Individual
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资助金额:$1.6万
-
财政年份:2020
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负责人:Poullis, Charalambos
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依托单位:
DEADALUS: Massive-scale urban reconstuction, classification, and rendering from remote sensor imagery
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批准号:515566-2017
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项目类别:Department of National Defence / NSERC Research Partnership
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资助金额:$9.47万
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财政年份:2019
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负责人:Poullis, Charalambos
-
依托单位:
Rapid and Automatic Reconstruction of Large-scale Areas
-
批准号:RGPIN-2016-06689
-
项目类别:Discovery Grants Program - Individual
-
资助金额:$1.6万
-
财政年份:2019
-
负责人:Poullis, Charalambos
-
依托单位:
Rapid and Automatic Reconstruction of Large-scale Areas
-
批准号:RGPIN-2016-06689
-
项目类别:Discovery Grants Program - Individual
-
资助金额:$1.6万
-
财政年份:2017
-
负责人:Poullis, Charalambos
-
依托单位:
Rapid and Automatic Reconstruction of Large-scale Areas
-
批准号:RGPIN-2016-06689
-
项目类别:Discovery Grants Program - Individual
-
资助金额:$1.6万
-
财政年份:2016
-
负责人:Poullis, Charalambos
-
依托单位:
海外基金