scholarly journals FPS-Net: A convolutional fusion network for large-scale LiDAR point cloud segmentation

2021 ◽  
Vol 176 ◽  
pp. 237-249
Author(s):  
Aoran Xiao ◽  
Xiaofei Yang ◽  
Shijian Lu ◽  
Dayan Guan ◽  
Jiaxing Huang
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Point Cloud Segmentation

scholarly journals Point Cloud Semantic Segmentation with Cross-Correction Features

2022 ◽  
Author(s):  
Yuehua Zhao ◽  
Ma Jie ◽  
Chong Nannan ◽  
Wen Junjie
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Large Scale ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Basic Unit ◽  
Semantic Features ◽  
Point Cloud Segmentation ◽  
Scale Point

Abstract Real time large scale point cloud segmentation is an important but challenging task for practical application like autonomous driving. Existing real time methods have achieved acceptance performance by aggregating local information. However, most of them only exploit local spatial information or local semantic information dependently, few considering the complementarity of both. In this paper, we propose a model named Spatial-Semantic Incorporation Network (SSI-Net) for real time large scale point cloud segmentation. A Spatial-Semantic Cross-correction (SSC) module is introduced in SSI-Net as a basic unit. High quality contextual features can be learned through SSC by correct and update semantic features using spatial cues, and vice verse. Adopting the plug-and-play SSC module, we design SSI-Net as an encoder-decoder architecture. To ensure efficiency, it also adopts a random sample based hierarchical network structure. Extensive experiments on several prevalent datasets demonstrate that our method can achieve state-of-the-art performance.

SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation

2021 ◽  
Author(s):  
Siqi Fan ◽  
Qiulei Dong ◽  
Fenghua Zhu ◽  
Yisheng Lv ◽  
Peijun Ye ◽  
...  
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Contextual Features ◽  
Point Cloud Segmentation ◽  
Scale Point

scholarly journals From a Point Cloud to a Simulation Model—Bayesian Segmentation and Entropy Based Uncertainty Estimation for 3D Modelling

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 301
Author(s):  
Christina Petschnigg ◽  
Markus Spitzner ◽  
Lucas Weitzendorf ◽  
Jürgen Pilz
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Object Identification ◽  
3D Modelling ◽  
Production Plant ◽  
Indoor Environments ◽  
Environment Model ◽  
Bayesian Segmentation ◽  
Point Cloud Segmentation ◽  
Existing Data

The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D. Thus, current environment models cannot be generated directly on the basis of existing data and a holistic approach on how to build such a factory model in a highly automated fashion is mostly non-existent. Major steps in generating an environment model of a production plant include data collection, data pre-processing and object identification as well as pose estimation. In this work, we elaborate on a methodical modelling approach, which starts with the digitalization of large-scale indoor environments and ends with the generation of a static environment or simulation model. The object identification step is realized using a Bayesian neural network capable of point cloud segmentation. We elaborate on the impact of the uncertainty information estimated by a Bayesian segmentation framework on the accuracy of the generated environment model. The steps of data collection and point cloud segmentation as well as the resulting model accuracy are evaluated on a real-world data set collected at the assembly line of a large-scale automotive production plant. The Bayesian segmentation network clearly surpasses the performance of the frequentist baseline and allows us to considerably increase the accuracy of the model placement in a simulation scene.

Deep Learning-Based Classification of Large-Scale Airborne LiDAR Point Cloud

2021 ◽  
pp. 1-15
Author(s):  
Mathieu Turgeon-Pelchat ◽  
Samuel Foucher ◽  
Yacine Bouroubi
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Large Scale ◽  
Airborne Lidar

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 Interactive Exploration of Large-Scale UI Datasets with Design Maps

2021 ◽  
Author(s):  
Luis A Leiva ◽  
Asutosh Hota ◽  
Antti Oulasvirta
Keyword(s):  
User Interface ◽  
Point Cloud ◽  
Large Scale ◽  
Mobile Apps ◽  
Online Resources ◽  
Design Tools ◽  
Support Design ◽  
Design Exploration ◽  
Clustering Techniques ◽  
Cloud Points

Abstract Designers are increasingly using online resources for inspiration. How to best support design exploration without compromising creativity? We introduce and study Design Maps, a class of point-cloud visualizations that makes large user interface datasets explorable. Design Maps are computed using dimensionality reduction and clustering techniques, which we analyze thoroughly in this paper. We present concepts for integrating Design Maps into design tools, including interactive visualization, local neighborhood exploration and functionality to integrate existing solutions to the design at hand. These concepts were implemented in a wireframing tool for mobile apps, which was evaluated with actual designers performing realistic tasks. Overall, designers find Design Maps supporting their creativity (avg. CSI score of 74/100) and indicate that the maps producing consistent whitespacing within cloud points are the most informative ones.

Semi-supervised point cloud segmentation using self-training with label confidence prediction

2021 ◽  
Vol 437 ◽  
pp. 227-237
Author(s):  
Hongyan Li ◽  
Zhengxing Sun ◽  
Yunjie Wu ◽  
Youcheng Song
Keyword(s):  
Point Cloud ◽  
Point Cloud Segmentation
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