Lode Jorissen, Patrik Goorts, G. Lafruit, P. Bekaert
{"title":"Nonuniform depth distribution selection with discrete Fourier transform","authors":"Lode Jorissen, Patrik Goorts, G. Lafruit, P. Bekaert","doi":"10.1145/2945078.2945133","DOIUrl":null,"url":null,"abstract":"In recent years there is a growing interest in the generation of virtual views from a limited set of input cameras. This is especially useful for applications such as Free Viewpoint Navigation and light field displays [Tanimoto 2015]. The latter often requires tens to hundreds of input views, while it is often not feasible to record with as many cameras. View interpolation algorithms often traverse a set of depths to find correspondences between the input images [Stankiewicz et al. 2013; Goorts et al. 2013]. Most algorithms choose a uniform set of depths to traverse (as shown in Figure 2(a)), but this often leads to an excessive amount of unnecessary calculations in regions where no objects are located. It also results in an increased amount of mismatches, and thus, inaccuracies in the generated views. These problems also occur when a too large depth range is selected. Hence, typically a depth range that encloses the scene tightly is manually selected to mitigate these errors. A depth distribution that organizes the depth layers around the objects in the scene, as shown in Figure 2(b), would reduce these errors and decrease the number of computations by reducing the number of depths to search through. [Goorts et al. 2013] determine a nonuniform global depth distribution by reusing the generated depth information from the previous time stamp. This makes the algorithm dependent on previous results.","PeriodicalId":417667,"journal":{"name":"ACM SIGGRAPH 2016 Posters","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGGRAPH 2016 Posters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2945078.2945133","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In recent years there is a growing interest in the generation of virtual views from a limited set of input cameras. This is especially useful for applications such as Free Viewpoint Navigation and light field displays [Tanimoto 2015]. The latter often requires tens to hundreds of input views, while it is often not feasible to record with as many cameras. View interpolation algorithms often traverse a set of depths to find correspondences between the input images [Stankiewicz et al. 2013; Goorts et al. 2013]. Most algorithms choose a uniform set of depths to traverse (as shown in Figure 2(a)), but this often leads to an excessive amount of unnecessary calculations in regions where no objects are located. It also results in an increased amount of mismatches, and thus, inaccuracies in the generated views. These problems also occur when a too large depth range is selected. Hence, typically a depth range that encloses the scene tightly is manually selected to mitigate these errors. A depth distribution that organizes the depth layers around the objects in the scene, as shown in Figure 2(b), would reduce these errors and decrease the number of computations by reducing the number of depths to search through. [Goorts et al. 2013] determine a nonuniform global depth distribution by reusing the generated depth information from the previous time stamp. This makes the algorithm dependent on previous results.
近年来,人们对从一组有限的输入摄像机生成虚拟视图越来越感兴趣。这对于自由视点导航和光场显示等应用尤其有用[Tanimoto 2015]。后者通常需要数十到数百个输入视图,而使用那么多摄像机进行记录通常是不可行的。视图插值算法通常遍历一组深度来找到输入图像之间的对应关系[Stankiewicz et al. 2013;Goorts et al. 2013]。大多数算法选择一组统一的深度来遍历(如图2(a)所示),但这通常会导致在没有对象所在的区域中进行过多不必要的计算。它还会导致不匹配的数量增加,从而导致生成的视图不准确。当选择的深度范围太大时也会出现这些问题。因此,通常手动选择一个紧密包围场景的深度范围来减轻这些错误。如图2(b)所示,在场景中围绕物体组织深度层的深度分布将减少这些错误,并通过减少搜索深度的数量来减少计算次数。[Goorts et al. 2013]通过重用从前一个时间戳生成的深度信息来确定非均匀的全球深度分布。这使得算法依赖于先前的结果。