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Enriched Soil Organic Carbon Underpins Long‐Term Stable Crop Productivity Across China's Croplands

Advanced Science, EarlyView.
Quantifying the nonlinear relationships and causal effects of soil organic carbon (SOC) on crop stability reveals specific critical thresholds and saturation points. This partitions the national SOC spectrum into three functional phases. Integrating these spatial delineations with high‐resolution maps of marginal gains pinpoints regions with the ...
Yuyang Huang, Yulin Shangguan, Zhongkui Luo, Peng Zhu, David Makowski, Zhenzhong Zeng, Qingxu Ma, Min Wei, Yuanyuan Du, Su Ye, Zhou Shi, Songchao Chen   +11 more
wiley   +1 more source

Sphere detection in Kinect point clouds via the 3D Hough transform

, 2013
Abuzaina, Anas, Nixon, Mark S., Alathari, Thamer, Carter, John N.   +3 more
core  

PnP-3D: A Plug-and-Play for 3D Point Clouds [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
With the help of the deep learning paradigm, many point cloud networks have been invented for visual analysis. However, there is great potential for development of these networks since the given information of point cloud data has not been fully exploited.
Shi Qiu, Saeed Anwar, Nick Barnes
exaly   +5 more sources

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Structure Perception in 3D Point Clouds

ACM Symposium on Applied Perception 2021, 2021
Understanding 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
Kenny Gruchalla, Sunand Raghupathi, Nicholas Brunhart-Lupo   +2 more
openaire   +1 more source

Surface approximation of a cloud of 3D points

Proceedings of 1994 IEEE 2nd CAD-Based Vision Workshop, 1995
We 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.
Chia-Wei Liao, Gérard G. Medioni
openaire   +1 more source

Meshfree Thinning of 3D Point Clouds

Foundations of Computational Mathematics, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nira Dyn, Armin Iske, Holger Wendland
openaire   +2 more sources

Algorithm for 3D Point Cloud Denoising

2009 Third International Conference on Genetic and Evolutionary Computing, 2009
The 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 ...
Wenming Huang, Yuanwang Li, Peizhi Wen, Xiaojun Wu 0004   +3 more
openaire   +2 more sources

On the segmentation of 3D LIDAR point clouds

2011 IEEE International Conference on Robotics and Automation, 2011
This 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.
Bertrand Douillard, James Patrick Underwood, Noah Kuntz, Vsevolod Vlaskine, Alastair James Quadros, Peter Morton, Alon Frenkel   +6 more
openaire   +2 more sources

Guided 3D point cloud filtering

Multimedia Tools and Applications, 2017
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 ...
Xian-Feng Han, Jesse S. Jin, Ming-Jie Wang, Wei Jiang 0038   +3 more
openaire   +1 more source

3D point cloud segmentation: A survey

2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM), 2013
3D point cloud segmentation is the process of classifying point clouds into multiple homogeneous regions, the points in the same region will have the same properties. The segmentation is challenging because of high redundancy, uneven sampling density, and lack explicit structure of point cloud data.
Anh Nguyen 0003, Bac Le
openaire   +1 more source