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Inverse Problems in Science and Engineering, 2011
A method based on point cloud smoothing approaches for detecting noise and outliers is introduced. This method firstly estimates thresholds according to points’ shifts after smoothing, secondly identifies outliers and noise whose shifts are more than the thresholds and lastly removes them and repeats the whole process.
Wenguang Hou, Taiwai Chan, Mingyue Ding
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A method based on point cloud smoothing approaches for detecting noise and outliers is introduced. This method firstly estimates thresholds according to points’ shifts after smoothing, secondly identifies outliers and noise whose shifts are more than the thresholds and lastly removes them and repeats the whole process.
Wenguang Hou, Taiwai Chan, Mingyue Ding
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PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
European Conference on Computer Vision, 2020Arguably one of the top success stories of deep learning is transfer learning. The finding that pre-training a network on a rich source set (eg., ImageNet) can help boost performance once fine-tuned on a usually much smaller target set, has been ...
Saining Xie +5 more
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Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing, 2004
Point clouds are one of the most primitive and fundamental surface representations. A popular source of point clouds are three dimensional shape acquisition devices such as laser range scanners. Another important field where point clouds are found is in the representation of high-dimensional manifolds by samples. With the increasing popularity and very
Facundo Mémoli, Guillermo Sapiro
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Point clouds are one of the most primitive and fundamental surface representations. A popular source of point clouds are three dimensional shape acquisition devices such as laser range scanners. Another important field where point clouds are found is in the representation of high-dimensional manifolds by samples. With the increasing popularity and very
Facundo Mémoli, Guillermo Sapiro
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arXiv.org
Recently, state space model (SSM) has gained great attention due to its promising performance, linear complexity, and long sequence modeling ability in both language and image domains.
Jiuming Liu +6 more
semanticscholar +1 more source
Recently, state space model (SSM) has gained great attention due to its promising performance, linear complexity, and long sequence modeling ability in both language and image domains.
Jiuming Liu +6 more
semanticscholar +1 more source
Self-Supervised Intra-Modal and Cross-Modal Contrastive Learning for Point Cloud Understanding
IEEE transactions on multimediaLearning effective representations from unlabeled data is a challenging task for point cloud understanding. As the human visual system can map concepts learned from 2D images to the 3D world, and inspired by recent multimodal research, we introduce data ...
Yue Wu +7 more
semanticscholar +1 more source
2016
Recent advances in acquisition technologies, such as LIDAR and photogrammetry, have brought back to popularity 3D point clouds in a lot of fields of application of Computer Graphics: Civil Engineering, Architecture, Topography, etc. These acquisition systems are producing an unprecedented amount of geometric data with additional attached information ...
Omar A. Mures +3 more
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Recent advances in acquisition technologies, such as LIDAR and photogrammetry, have brought back to popularity 3D point clouds in a lot of fields of application of Computer Graphics: Civil Engineering, Architecture, Topography, etc. These acquisition systems are producing an unprecedented amount of geometric data with additional attached information ...
Omar A. Mures +3 more
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Proceedings of the 26th International Conference on Scientific and Statistical Database Management, 2014
We introduce the concept of the point cloud database, a new kind of database system aimed primarily towards scientific applications. Many scientific observations, experiments, feature extraction algorithms and large-scale simulations produce enormous amounts of data that are better represented as sparse (but often highly-clustered) points in a k ...
László Dobos +4 more
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We introduce the concept of the point cloud database, a new kind of database system aimed primarily towards scientific applications. Many scientific observations, experiments, feature extraction algorithms and large-scale simulations produce enormous amounts of data that are better represented as sparse (but often highly-clustered) points in a k ...
László Dobos +4 more
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2017
Phase separation of a surfactant-loaded solution happens beyond a certain critical thermodynamic state (known as the cloud point), separating the hydrophobic-rich phase in a nonpolar microenvironment from the aqueous supernatant. The dye molecules are bounded to the surfactant and subsequently separated by changing the environmental factor (temperature
Sourav Mondal +2 more
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Phase separation of a surfactant-loaded solution happens beyond a certain critical thermodynamic state (known as the cloud point), separating the hydrophobic-rich phase in a nonpolar microenvironment from the aqueous supernatant. The dye molecules are bounded to the surfactant and subsequently separated by changing the environmental factor (temperature
Sourav Mondal +2 more
openaire +1 more source
2016
In this chapter, we focus on keypoint-based point cloud registration which has proven to be among the most efficient strategies for aligning pairs of overlapping scans. We present a novel and fully automated framework which consists of six components addressing (i) the generation of 2D image representations in the form of range and intensity images ...
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In this chapter, we focus on keypoint-based point cloud registration which has proven to be among the most efficient strategies for aligning pairs of overlapping scans. We present a novel and fully automated framework which consists of six components addressing (i) the generation of 2D image representations in the form of range and intensity images ...
openaire +1 more source