scholarly journals PIG-Net: Inception based deep learning architecture for 3D point cloud segmentation

2021 ◽  
Vol 95 ◽  
pp. 13-22
Author(s):  
Sindhu Hegde ◽  
Shankar Gangisetty
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
3D Point Cloud ◽  
Point Cloud 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

A novel 3D point cloud segmentation algorithm based on multi-resolution supervoxel and MGS

2021 ◽  
Vol 42 (22) ◽  
pp. 8492-8525
Author(s):  
Han Li ◽  
Yongmei Liu ◽  
Chaoguang Men ◽  
Yong Fang
Keyword(s):  
Point Cloud ◽  
Segmentation Algorithm ◽  
3D Point Cloud ◽  
Point Cloud Segmentation

scholarly journals Food Volume Estimation Based on Deep Learning View Synthesis from a Single Depth Map

Nutrients ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 2005 ◽  
Author(s):  
Frank Lo ◽  
Yingnan Sun ◽  
Jianing Qiu ◽  
Benny Lo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Point Cloud ◽  
Volume Estimation ◽  
Assessment System ◽  
View Synthesis ◽  
Depth Image ◽  
3D Point Cloud ◽  
Viewing Angle ◽  
3D Point Clouds

An objective dietary assessment system can help users to understand their dietary behavior and enable targeted interventions to address underlying health problems. To accurately quantify dietary intake, measurement of the portion size or food volume is required. For volume estimation, previous research studies mostly focused on using model-based or stereo-based approaches which rely on manual intervention or require users to capture multiple frames from different viewing angles which can be tedious. In this paper, a view synthesis approach based on deep learning is proposed to reconstruct 3D point clouds of food items and estimate the volume from a single depth image. A distinct neural network is designed to use a depth image from one viewing angle to predict another depth image captured from the corresponding opposite viewing angle. The whole 3D point cloud map is then reconstructed by fusing the initial data points with the synthesized points of the object items through the proposed point cloud completion and Iterative Closest Point (ICP) algorithms. Furthermore, a database with depth images of food object items captured from different viewing angles is constructed with image rendering and used to validate the proposed neural network. The methodology is then evaluated by comparing the volume estimated by the synthesized 3D point cloud with the ground truth volume of the object items.

scholarly journals Real-time 3D point cloud segmentation using Growing Neural Gas with Utility

2016 ◽  
Author(s):  
Yuichiro Toda ◽  
Zhaojie Ju ◽  
Hui Yu ◽  
Naoyuki Takesue ◽  
Kazuyoshi Wada ◽  
...  
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
3D Point Cloud ◽  
Growing Neural Gas ◽  
Neural Gas ◽  
Point Cloud Segmentation

A pothole detection method based on 3D point cloud segmentation

2020 ◽  
Author(s):  
Ying Du ◽  
Zuofeng Zhou ◽  
Qingquan Wu ◽  
Huimin Huang ◽  
Mingming Xu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Detection Method ◽  
3D Point Cloud ◽  
Point Cloud Segmentation ◽  
Pothole Detection

Point Encoder GAN: A deep learning model for 3D point cloud inpainting

2020 ◽  
Vol 384 ◽  
pp. 192-199 ◽  
Author(s):  
Yikuan Yu ◽  
Zitian Huang ◽  
Fei Li ◽  
Haodong Zhang ◽  
Xinyi Le
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Learning Model ◽  
3D Point Cloud ◽  
Deep Learning Model
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