DFSNet: A 3D Point Cloud Segmentation Network toward Trees Detection in an Orchard Scene. [PDF]
Sensors (Basel)Bu X +5 more
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Correction: Rezaei et al. Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping. <i>Sensors</i> 2024, <i>24</i>, 3534. [PDF]
Sensors (Basel)Rezaei S, Maier A, Arefi H.
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3D Point Cloud Object Detection Method Based on Multi-Scale Dynamic Sparse Voxelization. [PDF]
Sensors (Basel)Wang J, Liu Y, Zhu Y, Wang D, Zhang Y.
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A 3D Point Cloud Classification Method Based on Adaptive Graph Convolution and Global Attention. [PDF]
Sensors (Basel)Yue Y, Li X, Peng Y.
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Utilizing 3D Point Cloud Technology with Deep Learning for Automated Measurement and Analysis of Dairy Cows. [PDF]
Sensors (Basel)Lee JG +11 more
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Structure Perception in 3D Point Clouds
ACM Symposium on Applied Perception 2021, 2021Understanding human perception is critical to the design of effective visualizations. The relative benefits of using 2D versus 3D techniques for data visualization is a complex decision space, with varying levels of uncertainty and disagreement in both the literature and in practice. This study aims to add easily reproducible, empirical evidence on the
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Surface approximation of a cloud of 3D points
Proceedings of 1994 IEEE 2nd CAD-Based Vision Workshop, 1995We present an implementation of deformable models to approximate a 3-D surface given by a cloud of 3D points. It is an extension of our previous work on "B-snakes" (S. Menet, P. Saint-Marc, and G. Medioni, in Proceedings of Image Understanding Workshop, Pittsburgh, 1990, pp. 720-726; and C. W. Liao and G.
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Meshfree Thinning of 3D Point Clouds
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Algorithm for 3D Point Cloud Denoising
2009 Third International Conference on Genetic and Evolutionary Computing, 2009The raw data of point cloud produced by 3D scanning tools contains additive noise from various sources. This paper proposes a method for 3D unorganized point cloud denoising by making full use of the depth information of unorganized points and space analytic geometry theory, applying over-domain average method for 2D image of image denoising theory to ...
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On the segmentation of 3D LIDAR point clouds
2011 IEEE International Conference on Robotics and Automation, 2011This paper presents a set of segmentation methods for various types of 3D point clouds. Segmentation of dense 3D data (e.g. Riegl scans) is optimised via a simple yet efficient voxelisation of the space. Prior ground extraction is empirically shown to significantly improve segmentation performance. Segmentation of sparse 3D data (e.g.
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