Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05)最新文献_第5页

Incremental Catmull-Clark subdivision 增量卡特穆尔-克拉克细分

Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05) Pub Date : 2005-06-13 DOI: 10.1109/3DIM.2005.56

Hamid-Reza Pakdel, Faramarz F. Samavati

引用次数: 13

Gaussian scale-space dense disparity estimation with anisotropic disparity-field diffusion 具有各向异性差场扩散的高斯尺度空间密集差估计

Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05) Pub Date : 2005-06-13 DOI: 10.1109/3DIM.2005.50

Jangheon Kim, T. Sikora

{"title":"Gaussian scale-space dense disparity estimation with anisotropic disparity-field diffusion","authors":"Jangheon Kim, T. Sikora","doi":"10.1109/3DIM.2005.50","DOIUrl":"https://doi.org/10.1109/3DIM.2005.50","url":null,"abstract":"We present a new reliable dense disparity estimation algorithm which employs Gaussian scale-space with anisotropic disparity-field diffusion. This algorithm estimates edge-preserving dense disparity vectors using a diffusive method on iteratively Gaussian-filtered images with a scale, i.e. the Gaussian scale-space. While a Gaussian filter kernel generates a coarser resolution from stereo image pairs, only strong and meaningful boundaries are adoptively selected on the resolution of the filtered images. Then, coarse global disparity vectors are initialized using the boundary constraint. The per-pixel disparity vectors are iteratively obtained by the local adjustment of the global disparity vectors using an energy-minimization framework. The proposed algorithm preserves the boundaries while inner regions are smoothed using anisotropic disparity-field diffusion. In this work, the Gaussian scale-space efficiently avoids illegal matching on a large baseline by the restriction of the range. Moreover, it prevents the computation from iterating into local minima of ill-posed diffusion on large gradient areas e.g. shadow and texture region, etc. The experimental results prove the excellent localization performance preserving the disparity discontinuity of each object.","PeriodicalId":170883,"journal":{"name":"Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124711019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Spatio-temporal fusion of multiple view video rate 3D surfaces 多视频率三维曲面的时空融合

Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05) Pub Date : 2005-06-13 DOI: 10.1109/3DIM.2005.75

G. Collins, A. Hilton

引用次数: 1

Automatic registration of range images based on correspondence of complete plane patches 基于全平面补丁对应的距离图像自动配准

Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05) Pub Date : 2005-06-13 DOI: 10.1109/3DIM.2005.23

Wenfeng He, Wei Ma, H. Zha

引用次数: 32

Globally convergent range image registration by graph kernel algorithm 基于图核算法的全局收敛距离图像配准

Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05) Pub Date : 2005-06-13 DOI: 10.1109/3DIM.2005.51

R. Sára, I. Okatani, A. Sugimoto

引用次数: 18

3D registration by textured spin-images 纹理自旋图像的3D配准

Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05) Pub Date : 2005-06-13 DOI: 10.1109/3DIM.2005.5

N. Brusco, M. Andreetto, A. Giorgi, G. Cortelazzo

引用次数: 52

Image-gradient-guided real-time stereo on graphics hardware 图像梯度引导的实时立体图形硬件

Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05) Pub Date : 2005-06-13 DOI: 10.1109/3DIM.2005.55

Minglun Gong, Ruigang Yang

引用次数: 56

Bayesian modelling of camera calibration and reconstruction 摄像机标定与重建的贝叶斯建模

Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05) Pub Date : 2005-06-13 DOI: 10.1109/3DIM.2005.24

R. Sundareswara, P. Schrater

{"title":"Bayesian modelling of camera calibration and reconstruction","authors":"R. Sundareswara, P. Schrater","doi":"10.1109/3DIM.2005.24","DOIUrl":"https://doi.org/10.1109/3DIM.2005.24","url":null,"abstract":"Camera calibration methods, whether implicit or explicit, are a critical part of most 3D vision systems. These methods involve estimation of a model for the camera that produced the visual input, and subsequently to infer the 3D structure that gave rise to the input. However, in these systems the error in calibration is typically unknown, or if known, the effect of calibration error on subsequent processing (e.g. 3D reconstruction) is not accounted for. In this paper, we propose a Bayesian camera calibration method that explicitly computes calibration error, and we show how knowledge of this error can be used to improve the accuracy of subsequent processing. What distinguishes the work is the explicit computation of a posterior distribution on unknown camera parameters, rather than just a best estimate. Marginalizing (averaging) subsequent estimates by this posterior is shown to reduce reconstruction error over calibration approaches that rely on a single best estimate. The method is made practical using sampling techniques, that require only the evaluation of the calibration error function and the specification of priors. Samples with their corresponding probability weights can be used to produce better estimates of the camera parameters. Moreover, these samples can be directly used to improve estimates that rely on calibration information, like 3D reconstruction. We evaluate our method using simulated data for a structure from motion problem, in which the same point matches are used to calibrate the camera, estimate the motion, and reconstruct the 3D geometry. Our results show improved reconstruction over non-linear Camera calibration methods like the Maximum Likelihood estimate. Additionally, this approach scales much better in the face of increasingly noisy point matches.","PeriodicalId":170883,"journal":{"name":"Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133119428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Fitting of 3D circles and ellipses using a parameter decomposition approach 用参数分解方法拟合三维圆和椭圆

Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05) Pub Date : 2005-06-13 DOI: 10.1109/3DIM.2005.46

Xiaoyi Jiang, D. Cheng

{"title":"Fitting of 3D circles and ellipses using a parameter decomposition approach","authors":"Xiaoyi Jiang, D. Cheng","doi":"10.1109/3DIM.2005.46","DOIUrl":"https://doi.org/10.1109/3DIM.2005.46","url":null,"abstract":"Many optimization processes encounter a problem in efficiently reaching a global minimum or a near global minimum. Traditional methods such as Levenberg-Marquardt algorithm and trust-region method face the problems of dropping into local minima as well. On the other hand, some algorithms such as simulated annealing and genetic algorithm try to find a global minimum but they are mostly time-consuming. Without a good initialization, many optimization methods are unable to guarantee a global minimum result. We address a novel method in 3D circle and ellipse fitting, which alleviates the optimization problem. It can not only increase the probability of getting in global minima but also reduce the computation time. Based on our previous work, we decompose the parameters into two parts: one part of parameters can be solved by an analytic or a direct method and another part has to be solved by an iterative procedure. Via this scheme, the topography of optimization space is simplified and therefore, we reduce the number of local minima and the computation time. We experimentally compare our method with the traditional ones and show superior performance.","PeriodicalId":170883,"journal":{"name":"Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05)","volume":"519 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116260866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Registration of multiple range scans as a location recognition problem: hypothesis generation, refinement and verification 作为位置识别问题的多距离扫描配准:假设生成、改进和验证

Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05) Pub Date : 2005-06-13 DOI: 10.1109/3DIM.2005.68

B. J. King, Tomasz Malisiewicz, C. Stewart, R. Radke

{"title":"Registration of multiple range scans as a location recognition problem: hypothesis generation, refinement and verification","authors":"B. J. King, Tomasz Malisiewicz, C. Stewart, R. Radke","doi":"10.1109/3DIM.2005.68","DOIUrl":"https://doi.org/10.1109/3DIM.2005.68","url":null,"abstract":"This paper addresses the following version of the multiple range scan registration problem. A scanner with an associated intensity camera is placed at a series of locations throughout a large environment; scans are acquired at each location. The problem is to decide automatically which scans overlap and to estimate the parameters of the transformations aligning these scans. Our technique is based on (1) detecting and matching keypoints - distinctive locations in range and intensity images, (2) generating and refining a transformation estimate from each keypoint match, and (3) deciding if a given refined estimate is correct. While these steps are familiar, we present novel approaches to each. A new range keypoint technique is presented that uses spin images to describe holes in smooth surfaces. Intensity keypoints are detected using multiscale filters, described using intensity gradient histograms, and backprojected to form 3D keypoints. A hypothesized transformation is generated by matching a single keypoint from one scan to a single keypoint from another, and is refined using a robust form of the ICP algorithm in combination with controlled region growing. Deciding whether a refined transformation is correct is based on three criteria: alignment accuracy, visibility, and a novel randomness measure. Together these three steps produce good results in test scans of the Rensselaer campus.","PeriodicalId":170883,"journal":{"name":"Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124115928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22