scholarly journals Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation

2020 ◽  
pp. 564-580
Author(s):  
Tong He ◽  
Dong Gong ◽  
Zhi Tian ◽  
Chunhua Shen
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Instance Segmentation

Real-Time 3D object detection using improved convolutional neural network based on image-driven point cloud

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhiyong Gao ◽  
Jianhong Xiang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Point Cloud ◽  
3D Object ◽  
3D Object Detection ◽  
Instance Segmentation

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN is composed of the frustum sequence module, 3D instance segmentation module S-NET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module E-NET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms the state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved convolutional neural network (CNN) based on image-driven point clouds.

Mask-MCNet: Instance Segmentation in 3D Point Cloud of Intra-oral Scans

2019 ◽  
pp. 128-136 ◽  
Author(s):  
Farhad Ghazvinian Zanjani ◽  
David Anssari Moin ◽  
Frank Claessen ◽  
Teo Cherici ◽  
Sarah Parinussa ◽  
...  
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Instance Segmentation

End-to-End 3D Point Cloud Instance Segmentation Without Detection

2020 ◽  
Author(s):  
Haiyong Jiang ◽  
Feilong Yan ◽  
Jianfei Cai ◽  
Jianmin Zheng ◽  
Jun Xiao
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
End To End ◽  
Instance Segmentation

ATSGPN: adaptive threshold instance segmentation network in 3D point cloud

2020 ◽  
Author(s):  
Yu Sun ◽  
Zhicheng Wang ◽  
Jingjing Fei ◽  
Ling Chen ◽  
Gang Wei
Keyword(s):  
Point Cloud ◽  
Adaptive Threshold ◽  
3D Point Cloud ◽  
Instance Segmentation

scholarly journals Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Teng Miao ◽  
Weiliang Wen ◽  
Yinglun Li ◽  
Sheng Wu ◽  
Chao Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Training Dataset ◽  
Growth Stages ◽  
3D Point Cloud ◽  
Plant Structure ◽  
Data Annotation ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Different Growth Stages

Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.

scholarly journals Estimating Reference Bony Shape Models for Orthognathic Surgical Planning Using 3D Point-Cloud Deep Learning

2021 ◽  
pp. 1-1
Author(s):  
Deqiang Xiao ◽  
Chunfeng Lian ◽  
Han Deng ◽  
Tianshu Kuang ◽  
Qin Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Surgical Planning ◽  
3D Point Cloud ◽  
Shape Models
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