The Experts below are selected from a list of 73179 Experts worldwide ranked by ideXlab platform
Eckehard Steinbach - One of the best experts on this subject based on the ideXlab platform.
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real time compression of Point Cloud streams
International Conference on Robotics and Automation, 2012Co-Authors: Julius Kammerl, Radu Bogdan Rusu, Nico Blodow, Suat Gedikli, Michael Beetz, Eckehard SteinbachAbstract:We present a novel lossy compression approach for Point Cloud streams which exploits spatial and temporal redundancy within the Point data. Our proposed compression framework can handle general Point Cloud streams of arbitrary and varying size, Point order and Point density. Furthermore, it allows for controlling coding complexity and coding precision. To compress the Point Clouds, we perform a spatial decomposition based on octree data structures. Additionally, we present a technique for comparing the octree data structures of consecutive Point Clouds. By encoding their structural differences, we can successively extend the Point Clouds at the decoder. In this way, we are able to detect and remove temporal redundancy from the Point Cloud data stream. Our experimental results show a strong compression performance of a ratio of 14 at 1 mm coordinate precision and up to 40 at a coordinate precision of 9 mm.
Radu Bogdan Rusu - One of the best experts on this subject based on the ideXlab platform.
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real time compression of Point Cloud streams
International Conference on Robotics and Automation, 2012Co-Authors: Julius Kammerl, Radu Bogdan Rusu, Nico Blodow, Suat Gedikli, Michael Beetz, Eckehard SteinbachAbstract:We present a novel lossy compression approach for Point Cloud streams which exploits spatial and temporal redundancy within the Point data. Our proposed compression framework can handle general Point Cloud streams of arbitrary and varying size, Point order and Point density. Furthermore, it allows for controlling coding complexity and coding precision. To compress the Point Clouds, we perform a spatial decomposition based on octree data structures. Additionally, we present a technique for comparing the octree data structures of consecutive Point Clouds. By encoding their structural differences, we can successively extend the Point Clouds at the decoder. In this way, we are able to detect and remove temporal redundancy from the Point Cloud data stream. Our experimental results show a strong compression performance of a ratio of 14 at 1 mm coordinate precision and up to 40 at a coordinate precision of 9 mm.
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3D is here: Point Cloud library
IEEE International Conference on Robotics and Automation, 2011Co-Authors: Radu Bogdan Rusu, Steve Cousins, Sarah CousinsAbstract:With the advent of new, low-cost 3D sensing hardware such as the Kinect, and continued efforts in advanced Point Cloud processing, 3D perception gains more and more importance in robotics, as well as other fields. In this paper we present one of our most recent initiatives in the areas of Point Cloud perception: PCL (Point Cloud Library - http://PointClouds.org). PCL presents an advanced and extensive approach to the subject of 3D perception, and it's meant to provide support for all the common 3D building blocks that applications need. The library contains state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation. PCL is supported by an international community of robotics and perception researchers. We provide a brief walkthrough of PCL including its algorithmic capabilities and implementation strategies.
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3D is here: Point Cloud Library (PCL)
Proceedings - IEEE International Conference on Robotics and Automation, 2011Co-Authors: Radu Bogdan Rusu, Steve CousinsAbstract:With the advent of new, low-cost 3D sensing hardware such as the Kinect, and continued efforts in advanced Point Cloud processing, 3D perception gains more and more importance in robotics, as well as other fields. In this paper we present one of our most recent initiatives in the areas of Point Cloud perception: PCL (Point Cloud Library - http://PointClouds.org). PCL presents an advanced and extensive approach to the subject of 3D perception, and it's meant to provide support for all the common 3D building blocks that applications need. The library contains state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation. PCL is supported by an international community of robotics and perception researchers. We provide a brief walkthrough of PCL including its algorithmic capabilities and implementation strategies.
Julius Kammerl - One of the best experts on this subject based on the ideXlab platform.
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real time compression of Point Cloud streams
International Conference on Robotics and Automation, 2012Co-Authors: Julius Kammerl, Radu Bogdan Rusu, Nico Blodow, Suat Gedikli, Michael Beetz, Eckehard SteinbachAbstract:We present a novel lossy compression approach for Point Cloud streams which exploits spatial and temporal redundancy within the Point data. Our proposed compression framework can handle general Point Cloud streams of arbitrary and varying size, Point order and Point density. Furthermore, it allows for controlling coding complexity and coding precision. To compress the Point Clouds, we perform a spatial decomposition based on octree data structures. Additionally, we present a technique for comparing the octree data structures of consecutive Point Clouds. By encoding their structural differences, we can successively extend the Point Clouds at the decoder. In this way, we are able to detect and remove temporal redundancy from the Point Cloud data stream. Our experimental results show a strong compression performance of a ratio of 14 at 1 mm coordinate precision and up to 40 at a coordinate precision of 9 mm.
Didier Stricker - One of the best experts on this subject based on the ideXlab platform.
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Structured Low-Rank Matrix Factorization for Point-Cloud Denoising
2018 International Conference on 3D Vision (3DV), 2018Co-Authors: Kripasindhu Sarkar, Kiran Varanasi, Christian Theobalt, Florian Bernard, Didier StrickerAbstract:In this work we address the problem of Point-Cloud denoising where we assume that a given Point-Cloud comprises (noisy) Points that were sampled from an underlying surface that is to be denoised. We phrase the Point-Cloud denoising problem in terms of a dictionary learning framework. To this end, for a given Point-Cloud we (robustly) extract planar patches covering the entire Point-Cloud, where each patch contains a (noisy) description of the local structure of the underlying surface. Based on the general assumption that many of the local patches (in the noise-free Point-Cloud) contain redundant information (e.g. due to smoothness of the surface, or due to repetitive structures), we find a low-dimensional affine subspace that (approximately) explains the extracted (noisy) patches. Computationally, this is achieved by solving a structured low-rank matrix factorization problem, where we impose smoothness on the patch dictionary and sparsity on the coefficients. We experimentally demonstrate that our method outperforms existing denoising approaches in various noise scenarios.
Wei Jiang - One of the best experts on this subject based on the ideXlab platform.
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A comprehensive review of 3D Point Cloud descriptors.
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Xian-feng Hana, Ming-jie Wang, Wei JiangAbstract:The introduction of inexpensive 3D data acquisition devices has promisingly facilitated the wide availability and popularity of 3D Point Cloud, which attracts more attention on the effective extraction of novel 3D Point Cloud descriptors for accurate and efficient of 3D computer vision tasks. However, how to de- velop discriminative and robust feature descriptors from various Point Clouds remains a challenging task. This paper comprehensively investigates the exist- ing approaches for extracting 3D Point Cloud descriptors which are categorized into three major classes: local-based descriptor, global-based descriptor and hybrid-based descriptor. Furthermore, experiments are carried out to present a thorough evaluation of performance of several state-of-the-art 3D Point Cloud descriptors used widely in practice in terms of descriptiveness, robustness and efficiency.
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Guided 3D Point Cloud filtering
Multimedia Tools and Applications, 2017Co-Authors: Ming-jie Wang, Wei JiangAbstract:3D Point Cloud has gained significant attention in recent years. However, raw Point Clouds captured by 3D sensors are unavoidably contaminated with noise resulting in detrimental efforts on the practical applications. Although many widely used Point Cloud filters such as normal-based bilateral filter, can produce results as expected, they require a higher running time. Therefore, inspired by guided image filter, this paper takes the position information of the Point into account to derive the linear model with respect to guidance Point Cloud and filtered Point Cloud. Experimental results show that the proposed algorithm, which can successfully remove the undesirable noise while offering better performance in feature-preserving, is significantly superior to several state-of-the-art methods, particularly in terms of efficiency.
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Iterative guidance normal filter for Point Cloud
Multimedia Tools and Applications, 2017Co-Authors: Ming-jie Wang, Wei JiangAbstract:3D Point Clouds have become increasingly popular in recent year due to the rapid development of low-cost 3D sensors. One of the most interesting challenges is to filter Point Cloud, which undoubtedly becomes a crucial part of the Point Cloud processing pipeline. Based on normal information, this paper proposes a simple but effective Point Cloud filter framework. In this framework, a kd-tree structure is constructed for representing Point Cloud to search neighborhood and estimate normal for each Point at first. Then, iteratively performing the processing that a bilateral filter is applied to the normal field obtained from the previous iteration, using the same normal field as the guidance; afterward, adjusting Point positions is performed depending on the filtered normals. Experimental results indicate the effectiveness of our algorithms.
