Results 21 to 30 of about 61,541 (298)
Panicle-3D: Efficient Phenotyping Tool for Precise Semantic Segmentation of Rice Panicle Point Cloud
Plant Phenomics, 2021 The automated measurement of crop phenotypic parameters is of great significance to the quantitative study of crop growth. The segmentation and classification of crop point cloud help to realize the automation of crop phenotypic parameter measurement. At
Liang Gong +7 more
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Deep Learning Based Point Cloud Processing Techniques
IEEE Access, 2022 In this study, deep learning techniques and algorithms used in point cloud processing have been analysed. Methods, technical properties and algorithms developed for 3D Object Classification and Segmentation, 3D object detection and tracking and 3D scene ...
Abdurrahman Hazer, Remzi Yildirim
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SoftGroup for 3D Instance Segmentation on Point Clouds
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 To appear in CVPR ...
Thang Vu +4 more
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Splitting and Merging Based Multi-model Fitting for Point Cloud Segmentation [PDF]
Journal of Geodesy and Geoinformation Science, 2019 This paper deals with the massive point cloud segmentation processing technology on the basis of machine vision, which is the second essential factor for the intelligent data processing of three dimensional conformation in digital photogrammetry. In this
Liangpei ZHANG,Yun ZHANG,Zhenzhong CHEN,Peipei XIAO,Bin LUO
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FROM MULTI-VIEW TO POINT CLOUD SEGMENTATION: THE CASE STUDY OF VILLA ROBERTI BRUGINE [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Point cloud semantic segmentation is a key step for automatically deriving an informative building model from the 3D data reconstruction obtained by 3D surveying tools, such as laser scanners and photogrammetry. Such representation increases the richness
A. Masiero +3 more
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SEGCloud: Semantic Segmentation of 3D Point Clouds [PDF]
2017 International Conference on 3D Vision (3DV), 2017 3D semantic scene labeling is fundamental to agents operating in the real world. In particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent works leverage the capabilities of Neural Networks (NNs), but are limited to coarse voxel predictions and do not explicitly enforce global consistency.
Lyne P. Tchapmi +4 more
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Learning Polynomial-Based Separable Convolution for 3D Point Cloud Analysis
Sensors, 2021 Shape classification and segmentation of point cloud data are two of the most demanding tasks in photogrammetry and remote sensing applications, which aim to recognize object categories or point labels.
Ruixuan Yu, Jian Sun
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Survey of Point Cloud Semantic Segmentation Based on Deep Learning
Jisuanji kexue yu tansuo, 2021 In recent years, the popularity of depth sensors and 3D laserscanners has led to a rapid development of 3D point clouds processing methods. Semantic segmentation of point cloud, as a key step in understanding 3D scenes, has attracted extensive attention ...
JING Zhuangwei, GUAN Haiyan, ZANG Yufu, NI Huan, LI Dilong, YU Yongtao
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PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentation [PDF]
IEEE/RJS International Conference on Intelligent RObots and Systems, 2020 Following considerable development in 3D scanning technologies, many studies have recently been proposed with various approaches for 3D vision tasks, including some methods that utilize 2D convolutional neural networks (CNNs).
Juyoung Yang +5 more
semanticscholar +1 more source