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 In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D.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.

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 Indoor and Outdoor Backpack Mapping with Calibrated Pair of Velodyne LiDARs

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3944 ◽  
Author(s):  
Martin Velas ◽  
Michal Spanel ◽  
Tomas Sleziak ◽  
Jiri Habrovec ◽  
Adam Herout
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Laser Intensity ◽  
Information Modeling ◽  
Indoor Environments ◽  
Data Density ◽  
Universal Solution ◽  
Building Information ◽  
Sufficient Precision ◽  
Indoor And Outdoor

This paper presents a human-carried mapping backpack based on a pair of Velodyne LiDAR scanners. Our system is a universal solution for both large scale outdoor and smaller indoor environments. It benefits from a combination of two LiDAR scanners, which makes the odometry estimation more precise. The scanners are mounted under different angles, thus a larger space around the backpack is scanned. By fusion with GNSS/INS sub-system, the mapping of featureless environments and the georeferencing of resulting point cloud is possible. By deploying SoA methods for registration and the loop closure optimization, it provides sufficient precision for many applications in BIM (Building Information Modeling), inventory check, construction planning, etc. In our indoor experiments, we evaluated our proposed backpack against ZEB-1 solution, using FARO terrestrial scanner as the reference, yielding similar results in terms of precision, while our system provides higher data density, laser intensity readings, and scalability for large environments.

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 3D Modelling of Interior Spaces: Learning the Language of Indoor Architecture

2014 ◽  
Vol XL-5 ◽  
pp. 321-326 ◽  
Author(s):  
K. Khoshelham ◽  
L. Díaz-Vilariño
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
3D Models ◽  
3D Modelling ◽  
Shape Grammar ◽  
Real Point ◽  
Indoor Environments ◽  
Interior Spaces ◽  
Grammar Rules

3D models of indoor environments are important in many applications, but they usually exist only for newly constructed buildings. Automated approaches to modelling indoor environments from imagery and/or point clouds can make the process easier, faster and cheaper. We present an approach to 3D indoor modelling based on a shape grammar. We demonstrate that interior spaces can be modelled by iteratively placing, connecting and merging cuboid shapes. We also show that the parameters and sequence of grammar rules can be learned automatically from a point cloud. Experiments with simulated and real point clouds show promising results, and indicate the potential of the method in 3D modelling of large indoor environments.

scholarly journals Uncertainty Estimation in Deep Neural Networks for Point Cloud Segmentation in Factory Planning

Modelling ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 1-17
Author(s):  
Christina Petschnigg ◽  
Jürgen Pilz
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Scientific Data ◽  
Production Plant ◽  
Data Set ◽  
Factory Planning ◽  
3D Data ◽  
Future Production ◽  
Point Cloud Segmentation ◽  
Approximate Bayesian

The digital factory provides undoubtedly great potential for future production systems in terms of efficiency and effectivity. A key aspect on the way to realize the digital copy of a real factory is the understanding of complex indoor environments on the basis of three-dimensional (3D) data. In order to generate an accurate factory model including the major components, i.e., building parts, product assets, and process details, the 3D data that are collected during digitalization can be processed with advanced methods of deep learning. For instance, the semantic segmentation of a point cloud enables the identification of relevant objects within the environment. In this work, we propose a fully Bayesian and an approximate Bayesian neural network for point cloud segmentation. Both of the networks are used within a workflow in order to generate an environment model on the basis of raw point clouds. The Bayesian and approximate Bayesian networks allow us to analyse how different ways of estimating uncertainty in these networks improve segmentation results on raw point clouds. We achieve superior model performance for both, the Bayesian and the approximate Bayesian model compared to the frequentist one. This performance difference becomes even more striking when incorporating the networks’ uncertainty in their predictions. For evaluation, we use the scientific data set S3DIS as well as a data set, which was collected by the authors at a German automotive production plant. The methods proposed in this work lead to more accurate segmentation results and the incorporation of uncertainty information also makes this approach especially applicable to safety critical applications aside from our factory planning use case.

scholarly journals AUTOMATIC HERITAGE BUILDING POINT CLOUD SEGMENTATION AND CLASSIFICATION USING GEOMETRICAL RULES

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 821-827 ◽  
Author(s):  
A. Murtiyoso ◽  
P. Grussenmeyer
Keyword(s):  
Success Rate ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Modelling ◽  
Data Sets ◽  
Architectural Elements ◽  
Point Cloud Segmentation ◽  
Scale Levels ◽  
Heritage Building ◽  
Modelling Process

<p><strong>Abstract.</strong> The segmentation of a point cloud presents an important step in the 3D modelling process of heritage structures. This is true in many scale levels, including the segmentation, identification, and classification of architectural elements from the point cloud of a building. In this regard, historical buildings often present complex elements which render the 3D modelling process longer when performed manually. The aim of this paper is to explore approaches based on certain common geometric rules in order to segment, identify, and classify point clouds into architectural elements. In particular, the detection of attics and structural supports (i.e. columns and piers) will be addressed. Results show that the developed algorithm manages to detect supports in three separate data sets representing three different types of architecture. The algorithm also managed to identify the type of support and divide them into two groups: columns and piers. Overall, the developed method provides a fast and simple approach to classify point clouds automatically into several classes, with a mean success rate of 81.61&amp;thinsp;% and median success rate of 85.61&amp;thinsp% for three tested data sets.</p>

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 Semantic 3D Mapping from Deep Image Segmentation

2021 ◽  
Vol 11 (4) ◽  
pp. 1953
Author(s):  
Francisco Martín ◽  
Fernando González ◽  
José Miguel Guerrero ◽  
Manuel Fernández ◽  
Jonatan Ginés
Keyword(s):  
Point Cloud ◽  
Autonomous Mobile Robots ◽  
Semantic Mapping ◽  
Indoor Environments ◽  
3D Mapping ◽  
Direct Mapping ◽  
Learning Techniques ◽  
3D Camera ◽  
Deep Image ◽  
Boundary Pixel

The perception and identification of visual stimuli from the environment is a fundamental capacity of autonomous mobile robots. Current deep learning techniques make it possible to identify and segment objects of interest in an image. This paper presents a novel algorithm to segment the object’s space from a deep segmentation of an image taken by a 3D camera. The proposed approach solves the boundary pixel problem that appears when a direct mapping from segmented pixels to their correspondence in the point cloud is used. We validate our approach by comparing baseline approaches using real images taken by a 3D camera, showing that our method outperforms their results in terms of accuracy and reliability. As an application of the proposed algorithm, we present a semantic mapping approach for a mobile robot’s indoor environments.

Sign in / Sign up

Export Citation Format

Share Document