Results 11 to 20 of about 19,175 (257)
Point Cloud Semantic Segmentation Method Based on Block Feature Fusion [PDF]
Jisuanji gongcheng, 2022 A semantic segmentation method integrating block features is proposed to reduce the computational complexity of semantic segmentation of multi-class 3D objects in outdoor large-scale point cloud scenes.The square grid segmentation method is used to ...
GAO Qingji, LI Tianhao, XING Zhiwei, LIU Peipei
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Deep Segmentation of Point Clouds of Wheat [PDF]
Frontiers in Plant Science, 2021 The 3D analysis of plants has become increasingly effective in modeling the relative structure of organs and other traits of interest. In this paper, we introduce a novel pattern-based deep neural network, Pattern-Net, for segmentation of point clouds of wheat.
Morteza Ghahremani +6 more
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RESEARCH ON IMPROVED REGION GROWING POINT CLOUD ALGORITHM [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 The effective segmentation of point clouds is a prerequisite for surface reconstruction, blind spot repair, and so on. Among them, regional growth is widely used due to its simple and easy to implement algorithm.
C. L. Kang +9 more
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Instance Segmentation of LiDAR Point Clouds [PDF]
2020 IEEE International Conference on Robotics and Automation (ICRA), 2020 We propose a robust baseline method for instance segmentation which are specially designed for large-scale outdoor LiDAR point clouds. Our method includes a novel dense feature encoding technique, allowing the localization and segmentation of small, far-away objects, a simple but effective solution for single-shot instance prediction and effective ...
Feihu Zhang +6 more
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Learning to Optimally Segment Point Clouds [PDF]
IEEE Robotics and Automation Letters, 2020 We focus on the problem of class-agnostic instance segmentation of LiDAR point clouds. We propose an approach that combines graph-theoretic search with data-driven learning: it searches over a set of candidate segmentations and returns one where individual segments score well according to a data-driven point-based model of "objectness".
Peiyun Hu, David Held, Deva Ramanan
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Three-dimensional Point Cloud Model Segmentation Based on Significance and Weak Convexity [PDF]
Jisuanji gongcheng, 2018 The existing three-dimensional point cloud model segmentation algorithms cannot segment large components and small components at the same time.Aiming at this problem,in this paper,a segmentation method is proposed based on the significance and weak ...
ZHENG Lele,HAN Huiyan,HAN Xie
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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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Nonlocal segmentation of point clouds with graphs [PDF]
2013 IEEE Global Conference on Signal and Information Processing, 2013 In this paper, we propose a nonlocal approach based on graphs to segment raw point clouds as a particular class of graph signals. Using the framework of Partial difference Equations (PdEs), we propose a transcription on graphs of recent continuous global active contours along with a minimization algorithm.
Francois Lozes +3 more
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Projection-Based Point Convolution for Efficient Point Cloud Segmentation [PDF]
IEEE Access, 2022 Published in IEEE Access (Early Access)
Pyunghwan Ahn +4 more
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Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds [PDF]
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013 Unsupervised over-segmentation of an image into regions of perceptually similar pixels, known as super pixels, is a widely used preprocessing step in segmentation algorithms. Super pixel methods reduce the number of regions that must be considered later by more computationally expensive algorithms, with a minimal loss of information.
Papon J. +3 more
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