The Experts below are selected from a list of 102891 Experts worldwide ranked by ideXlab platform
Claudio T. Silva - One of the best experts on this subject based on the ideXlab platform.
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A Survey of Surface Reconstruction from Point Clouds
Computer Graphics Forum, 2016Co-Authors: Matthew Berger, Joshua A. Levine, Andrea Tagliasacchi, Lee M. Seversky, Pierre Alliez, Gaël Guennebaud, Andrei Sharf, Claudio T. SilvaAbstract:The area of Surface Reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by Surface Reconstruction is to recover the digital representation of a physical shape that has been scanned, where the scanned data contain a wide variety of defects. While much of the earlier work has been focused on reconstructing a piece-wise smooth representation of the original shape, recent work has taken on more specialized priors to address significantly challenging data imperfections, where the Reconstruction can take on different representations-not necessarily the explicit geometry. We survey the field of Surface Reconstruction, and provide a categorization with respect to priors, data imperfections and Reconstruction output. By considering a holistic view of Surface Reconstruction, we show a detailed characterization of the field, highlight similarities between diverse Reconstruction techniques and provide directions for future work in Surface Reconstruction.
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A benchmark for Surface Reconstruction
ACM Transactions on Graphics, 2013Co-Authors: Matthew Berger, Luis Gustavo Nonato, Gabriel Taubin, Joshua A. Levine, Claudio T. SilvaAbstract:We present a benchmark for the evaluation and comparison of algorithms which reconstruct a Surface from point cloud data. Although a substantial amount of effort has been dedicated to the problem of Surface Reconstruction, a comprehensive means of evaluating this class of algorithms is noticeably absent.We propose a simple pipeline for measuring Surface Reconstruction algorithms, consisting of three main phases: Surface modeling, sampling, and evaluation. We use implicit Surfaces for modeling shapes which are capable of representing details of varying size and sharp features. From these implicit Surfaces, we produce point clouds by synthetically generating range scans which resemble realistic scan data produced by an optical triangulation scanner. We validate our synthetic sampling scheme by comparing against scan data produced by a commercial optical laser scanner, where we scan a 3D-printed version of the original Surface. Last, we perform evaluation by comparing the output reconstructed Surface to a dense uniformly-distributed sampling of the implicit Surface. We decompose our benchmark into two distinct sets of experiments. The first set of experiments measures Reconstruction against point clouds of complex shapes sampled under a wide variety of conditions. Although these experiments are quite useful for comparison, they lack a fine-grain analysis. To complement this, the second set of experiments measures specific properties of Surface Reconstruction, in terms of sampling characteristics and Surface features. Together, these experiments depict a detailed examination of the state of Surface Reconstruction algorithms.
Turppa Tuomas - One of the best experts on this subject based on the ideXlab platform.
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Multisource point clouds, point simplification and Surface Reconstruction
'MDPI AG', 2019Co-Authors: Zhu Lingli, Kukko Antero, Virtanen, Juho Pekka, Hyyppä Juha, Kaartinen Harri, Hyyppä Hannu, Turppa TuomasAbstract:As data acquisition technology continues to advance, the improvement and upgrade of the algorithms for Surface Reconstruction are required. In this paper, we utilized multiple terrestrial Light Detection And Ranging (Lidar) systems to acquire point clouds with different levels of complexity, namely dynamic and rigid targets for Surface Reconstruction. We propose a robust and effective method to obtain simplified and uniform resample points for Surface Reconstruction. The method was evaluated. A point reduction of up to 99.371% with a standard deviation of 0.2 cm was achieved. In addition, well-known Surface Reconstruction methods, i.e., Alpha shapes, Screened Poisson Reconstruction (SPR), the Crust, and Algebraic point set Surfaces (APSS Marching Cubes), were utilized for object Reconstruction. We evaluated the benefits in exploiting simplified and uniform points, as well as different density points, for Surface Reconstruction. These Reconstruction methods and their capacities in handling data imperfections were analyzed and discussed. The findings are that i) the capacity of Surface Reconstruction in dealing with diverse objects needs to be improved; ii) when the number of points reaches the level of millions (e.g., approximately five million points in our data), point simplification is necessary, as otherwise, the Reconstruction methods might fail; iii) for some Reconstruction methods, the number of input points is proportional to the number of output meshes; but a few methods are in the opposite; iv) all Reconstruction methods are beneficial from the reduction of running time; and v) a balance between the geometric details and the level of smoothing is needed. Some methods produce detailed and accurate geometry, but their capacity to deal with data imperfection is poor, while some other methods exhibit the opposite characteristics.Peer reviewe
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Multisource Point Clouds, Point Simplification and Surface Reconstruction
'MDPI AG', 2019Co-Authors: Zhu Lingli, Kukko Antero, Hyyppä Juha, Kaartinen Harri, Hyyppä Hannu, Virtanen Juho-pekka, Turppa TuomasAbstract:As data acquisition technology continues to advance, the improvement and upgrade of the algorithms for Surface Reconstruction are required. In this paper, we utilized multiple terrestrial Light Detection And Ranging (Lidar) systems to acquire point clouds with different levels of complexity, namely dynamic and rigid targets for Surface Reconstruction. We propose a robust and effective method to obtain simplified and uniform resample points for Surface Reconstruction. The method was evaluated. A point reduction of up to 99.371% with a standard deviation of 0.2 cm was achieved. In addition, well-known Surface Reconstruction methods, i.e., Alpha shapes, Screened Poisson Reconstruction (SPR), the Crust, and Algebraic point set Surfaces (APSS Marching Cubes), were utilized for object Reconstruction. We evaluated the benefits in exploiting simplified and uniform points, as well as different density points, for Surface Reconstruction. These Reconstruction methods and their capacities in handling data imperfections were analyzed and discussed. The findings are that (i) the capacity of Surface Reconstruction in dealing with diverse objects needs to be improved; (ii) when the number of points reaches the level of millions (e.g., approximately five million points in our data), point simplification is necessary, as otherwise, the Reconstruction methods might fail; (iii) for some Reconstruction methods, the number of input points is proportional to the number of output meshes; but a few methods are in the opposite; (iv) all Reconstruction methods are beneficial from the reduction of running time; and (v) a balance between the geometric details and the level of smoothing is needed. Some methods produce detailed and accurate geometry, but their capacity to deal with data imperfection is poor, while some other methods exhibit the opposite characteristics
Tie Zhou - One of the best experts on this subject based on the ideXlab platform.
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VLSM - A Surface Reconstruction method for highly noisy point clouds
Lecture Notes in Computer Science, 2005Co-Authors: Hongkai Zhao, Ming Jiang, Shulin Zhou, Tie ZhouAbstract:In this paper we propose a Surface Reconstruction method for highly noisy and non-uniform data based on minimal Surface model and tensor voting method. To deal with ill-posedness, noise and/or other uncertainties in the data we processes the raw data first using tensor voting before we do Surface Reconstruction. The tensor voting procedure allows more global and robust communications among the data to extract coherent geometric features and saliency independent of the Surface Reconstruction. These extracted information will be used to preprocess the data and to guide the final Surface Reconstruction. Numerically the level set method is used for Surface Reconstruction. Our method can handle complicated topology as well as highly noisy and/or non-uniform data set. Moreover, improvements of efficiency in implementing the tensor voting method are also proposed. We demonstrate the ability of our method using synthetic and real data.
Matthew Berger - One of the best experts on this subject based on the ideXlab platform.
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A Survey of Surface Reconstruction from Point Clouds
Computer Graphics Forum, 2016Co-Authors: Matthew Berger, Joshua A. Levine, Andrea Tagliasacchi, Lee M. Seversky, Pierre Alliez, Gaël Guennebaud, Andrei Sharf, Claudio T. SilvaAbstract:The area of Surface Reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by Surface Reconstruction is to recover the digital representation of a physical shape that has been scanned, where the scanned data contain a wide variety of defects. While much of the earlier work has been focused on reconstructing a piece-wise smooth representation of the original shape, recent work has taken on more specialized priors to address significantly challenging data imperfections, where the Reconstruction can take on different representations-not necessarily the explicit geometry. We survey the field of Surface Reconstruction, and provide a categorization with respect to priors, data imperfections and Reconstruction output. By considering a holistic view of Surface Reconstruction, we show a detailed characterization of the field, highlight similarities between diverse Reconstruction techniques and provide directions for future work in Surface Reconstruction.
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State of the Art in Surface Reconstruction from Point Clouds
2014Co-Authors: Matthew Berger, Andrea Tagliasacchi, Lee M. Seversky, Pierre Alliez, Andrei Sharf, Joshua Levine, Claudio SilvaAbstract:The area of Surface Reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by Surface Reconstruction is to recover the digital representation of a physical shape that has been scanned, where the scanned data contains a wide variety of defects. While much of the earlier work has been focused on reconstructing a piece-wise smooth representation of the original shape, recent work has taken on more specialized priors to address significantly challenging data imperfections, where the Reconstruction can take on different representations -- not necessarily the explicit geometry. This state-of-the-art report surveys the field of Surface Reconstruction, providing a categorization with respect to priors, data imperfections, and Reconstruction output. By considering a holistic view of Surface Reconstruction, this report provides a detailed characterization of the field, highlights similarities between diverse Reconstruction techniques, and provides directions for future work in Surface Reconstruction.
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A benchmark for Surface Reconstruction
ACM Transactions on Graphics, 2013Co-Authors: Matthew Berger, Luis Gustavo Nonato, Gabriel Taubin, Joshua A. Levine, Claudio T. SilvaAbstract:We present a benchmark for the evaluation and comparison of algorithms which reconstruct a Surface from point cloud data. Although a substantial amount of effort has been dedicated to the problem of Surface Reconstruction, a comprehensive means of evaluating this class of algorithms is noticeably absent.We propose a simple pipeline for measuring Surface Reconstruction algorithms, consisting of three main phases: Surface modeling, sampling, and evaluation. We use implicit Surfaces for modeling shapes which are capable of representing details of varying size and sharp features. From these implicit Surfaces, we produce point clouds by synthetically generating range scans which resemble realistic scan data produced by an optical triangulation scanner. We validate our synthetic sampling scheme by comparing against scan data produced by a commercial optical laser scanner, where we scan a 3D-printed version of the original Surface. Last, we perform evaluation by comparing the output reconstructed Surface to a dense uniformly-distributed sampling of the implicit Surface. We decompose our benchmark into two distinct sets of experiments. The first set of experiments measures Reconstruction against point clouds of complex shapes sampled under a wide variety of conditions. Although these experiments are quite useful for comparison, they lack a fine-grain analysis. To complement this, the second set of experiments measures specific properties of Surface Reconstruction, in terms of sampling characteristics and Surface features. Together, these experiments depict a detailed examination of the state of Surface Reconstruction algorithms.
Zhu Lingli - One of the best experts on this subject based on the ideXlab platform.
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Multisource point clouds, point simplification and Surface Reconstruction
'MDPI AG', 2019Co-Authors: Zhu Lingli, Kukko Antero, Virtanen, Juho Pekka, Hyyppä Juha, Kaartinen Harri, Hyyppä Hannu, Turppa TuomasAbstract:As data acquisition technology continues to advance, the improvement and upgrade of the algorithms for Surface Reconstruction are required. In this paper, we utilized multiple terrestrial Light Detection And Ranging (Lidar) systems to acquire point clouds with different levels of complexity, namely dynamic and rigid targets for Surface Reconstruction. We propose a robust and effective method to obtain simplified and uniform resample points for Surface Reconstruction. The method was evaluated. A point reduction of up to 99.371% with a standard deviation of 0.2 cm was achieved. In addition, well-known Surface Reconstruction methods, i.e., Alpha shapes, Screened Poisson Reconstruction (SPR), the Crust, and Algebraic point set Surfaces (APSS Marching Cubes), were utilized for object Reconstruction. We evaluated the benefits in exploiting simplified and uniform points, as well as different density points, for Surface Reconstruction. These Reconstruction methods and their capacities in handling data imperfections were analyzed and discussed. The findings are that i) the capacity of Surface Reconstruction in dealing with diverse objects needs to be improved; ii) when the number of points reaches the level of millions (e.g., approximately five million points in our data), point simplification is necessary, as otherwise, the Reconstruction methods might fail; iii) for some Reconstruction methods, the number of input points is proportional to the number of output meshes; but a few methods are in the opposite; iv) all Reconstruction methods are beneficial from the reduction of running time; and v) a balance between the geometric details and the level of smoothing is needed. Some methods produce detailed and accurate geometry, but their capacity to deal with data imperfection is poor, while some other methods exhibit the opposite characteristics.Peer reviewe
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Multisource Point Clouds, Point Simplification and Surface Reconstruction
'MDPI AG', 2019Co-Authors: Zhu Lingli, Kukko Antero, Hyyppä Juha, Kaartinen Harri, Hyyppä Hannu, Virtanen Juho-pekka, Turppa TuomasAbstract:As data acquisition technology continues to advance, the improvement and upgrade of the algorithms for Surface Reconstruction are required. In this paper, we utilized multiple terrestrial Light Detection And Ranging (Lidar) systems to acquire point clouds with different levels of complexity, namely dynamic and rigid targets for Surface Reconstruction. We propose a robust and effective method to obtain simplified and uniform resample points for Surface Reconstruction. The method was evaluated. A point reduction of up to 99.371% with a standard deviation of 0.2 cm was achieved. In addition, well-known Surface Reconstruction methods, i.e., Alpha shapes, Screened Poisson Reconstruction (SPR), the Crust, and Algebraic point set Surfaces (APSS Marching Cubes), were utilized for object Reconstruction. We evaluated the benefits in exploiting simplified and uniform points, as well as different density points, for Surface Reconstruction. These Reconstruction methods and their capacities in handling data imperfections were analyzed and discussed. The findings are that (i) the capacity of Surface Reconstruction in dealing with diverse objects needs to be improved; (ii) when the number of points reaches the level of millions (e.g., approximately five million points in our data), point simplification is necessary, as otherwise, the Reconstruction methods might fail; (iii) for some Reconstruction methods, the number of input points is proportional to the number of output meshes; but a few methods are in the opposite; (iv) all Reconstruction methods are beneficial from the reduction of running time; and (v) a balance between the geometric details and the level of smoothing is needed. Some methods produce detailed and accurate geometry, but their capacity to deal with data imperfection is poor, while some other methods exhibit the opposite characteristics
