Image Detector Based Automatic 3D Data Labeling and Training for Vehicle Detection on Point Cloud

2019 ◽  
Author(s):  
Zhengyong Chen ◽  
Qinghai Liao ◽  
Zhe Wang ◽  
Yang Liu ◽  
Ming Liu
Keyword(s):  
Point Cloud ◽  
Vehicle Detection ◽  
Image Detector ◽  
3D Data ◽  
And Training

scholarly journals An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios

2021 ◽  
Vol 13 (10) ◽  
pp. 1985
Author(s):  
Emre Özdemir ◽  
Fabio Remondino ◽  
Alessandro Golkar
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Computational Power ◽  
Specific Data ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Current State ◽  
Data Source ◽  
Point Cloud Classification ◽  
And Training

With recent advances in technologies, deep learning is being applied more and more to different tasks. In particular, point cloud processing and classification have been studied for a while now, with various methods developed. Some of the available classification approaches are based on specific data source, like LiDAR, while others are focused on specific scenarios, like indoor. A general major issue is the computational efficiency (in terms of power consumption, memory requirement, and training/inference time). In this study, we propose an efficient framework (named TONIC) that can work with any kind of aerial data source (LiDAR or photogrammetry) and does not require high computational power while achieving accuracy on par with the current state of the art methods. We also test our framework for its generalization ability, showing capabilities to learn from one dataset and predict on unseen aerial scenarios.

scholarly journals SPARSE POINT CLOUD FILTERING BASED ON COVARIANCE FEATURES

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 465-472 ◽  
Author(s):  
E. M. Farella ◽  
A. Torresani ◽  
F. Remondino
Keyword(s):  
Point Cloud ◽  
Bundle Adjustment ◽  
Geometric Features ◽  
Local Behaviour ◽  
High Entropy ◽  
3D Data ◽  
Automatic Filtering ◽  
Noisy 3D Data ◽  
Low Entropy ◽  
Filtering Procedure

<p><strong>Abstract.</strong> This work presents an extended photogrammetric pipeline aimed to improve 3D reconstruction results. Standard photogrammetric pipelines can produce noisy 3D data, especially when images are acquired with various sensors featuring different properties. In this paper, we propose an automatic filtering procedure based on some geometric features computed on the sparse point cloud created within the bundle adjustment phase. Bad 3D tie points and outliers are detected and removed, relying on micro and macro-clusters analyses. Clusters are built according to the prevalent dimensionality class (1D, 2D, 3D) assigned to low-entropy points, and corresponding to the main linear, planar o scatter local behaviour of the point cloud. While the macro-clusters analysis removes smallsized clusters and high-entropy points, in the micro-clusters investigation covariance features are used to verify the inner coherence of each point to the assigned class. Results on heritage scenarios are presented and discussed.</p>

Rotation Aware 3D Point Cloud Vehicle Detection

2021 ◽  
pp. 83-93
Author(s):  
Hongchao Feng ◽  
Yunqian He ◽  
Guihua Xia
Keyword(s):  
Point Cloud ◽  
Vehicle Detection ◽  
3D Point Cloud

scholarly journals An overview of ongoing point cloud compression standardization activities: video-based (V-PCC) and geometry-based (G-PCC)

2020 ◽  
Vol 9 ◽  
Author(s):  
D. Graziosi ◽  
O. Nakagami ◽  
S. Kuma ◽  
A. Zaghetto ◽  
T. Suzuki ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Cultural Heritage ◽  
Motion Picture ◽  
Point Cloud ◽  
Autonomous Driving ◽  
Data Representation ◽  
Media Content ◽  
International Standard ◽  
3D Data ◽  
Immersive Media

Abstract This article presents an overview of the recent standardization activities for point cloud compression (PCC). A point cloud is a 3D data representation used in diverse applications associated with immersive media including virtual/augmented reality, immersive telepresence, autonomous driving and cultural heritage archival. The international standard body for media compression, also known as the Motion Picture Experts Group (MPEG), is planning to release in 2020 two PCC standard specifications: video-based PCC (V-CC) and geometry-based PCC (G-PCC). V-PCC and G-PCC will be part of the ISO/IEC 23090 series on the coded representation of immersive media content. In this paper, we provide a detailed description of both codec algorithms and their coding performances. Moreover, we will also discuss certain unique aspects of point cloud compression.

scholarly journals Real-Time Vehicle Detection Algorithm Based on Vision and Lidar Point Cloud Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hai Wang ◽  
Xinyu Lou ◽  
Yingfeng Cai ◽  
Yicheng Li ◽  
Long Chen
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Depth Information ◽  
Detection Accuracy ◽  
Classification Rate

Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.

scholarly journals THE CONTRIBUTION OF GEOMATIC TECHNOLOGIES TO BIM

2019 ◽  
Vol XLII-5/W3 ◽  
pp. 85-89
Author(s):  
K. Pavelka ◽  
E. Matoušková ◽  
K. Pavelka jr.
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Modern Technology ◽  
Data Conversion ◽  
Digital Information ◽  
Historical Building ◽  
Data Set ◽  
Common Definition ◽  
3D Data ◽  
Building Information

Abstract. There are many definitions of the commonly used abbreviation BIM, but one can say that each user or data supplier has different idea about it. There can be an economic view, or other aspects like surveying, material, engineering, maintenance, etc. The common definition says that Building Information Modelling or Building Information Management (BIM) is a digital model representing a physical and functional object with its characteristics. The model serves as a database of object information for its design, construction and operation over its life cycle, i.e. from the initial concept to the removal of the building. BIM is a collection of interconnected digital information in both protected and open formats, recording graphical and non-graphical data on model elements. There are two facets: a) BIM created simultaneously with the project, or project designed directly in BIM (it is typical of new objects designed in CAD systems - for example in the Revit software) or b) BIM for old or historical objects. The former is a modern technology, which is nowadays used worldwide. From the engineer’s perspective, the issue is the creation of BIM for older objects. In this case, it is crucial to obtain a precise 3D data set - complex 3D documentation of an object is needed and it is created using various surveying techniques. The most popular technique is laser scanning or digital automatic photogrammetry, from which a point cloud is derived. But this is not the main result. While classical geodesy gives selective localized information, the above-mentioned technologies give unselected information and provide huge datasets. Fully automatic technologies that would select important information from the point cloud are still under development. This seems to be a task for the coming years. Large amounts of data can be acquired automatically and quickly, but getting the expected information is another matter. These problems will be analysed in this paper. Data conversion to BIM, especially for older objects, will be shown on several case studies. The first is an older technical building complex transferred to BIM, the second one is a historical building, and the third one will be a historic medieval bridge (Charles Bridge in Prague). The last part of this paper will refer to aspects and benefits of using Virtual Reality in BIM.

scholarly journals A Novel Recursive Non-Parametric DBSCAN Algorithm for 3D Data Analysis with an Application in Rockfall Detection

2021 ◽  
Vol 16 (4) ◽  
pp. 579-587
Author(s):  
Pitisit Dillon ◽  
Pakinee Aimmanee ◽  
Akihiko Wakai ◽  
Go Sato ◽  
Hoang Viet Hung ◽  
...  
Keyword(s):  
Point Cloud ◽  
Crack Detection ◽  
Spatial Clustering ◽  
Point Clouds ◽  
Divide And Conquer ◽  
Dbscan Algorithm ◽  
3D Data ◽  
Data Points ◽  
Clustering Data ◽  
Better Than

The density-based spatial clustering of applications with noise (DBSCAN) algorithm is a well-known algorithm for spatial-clustering data point clouds. It can be applied to many applications, such as crack detection, rockfall detection, and glacier movement detection. Traditional DBSCAN requires two predefined parameters. Suitable values of these parameters depend upon the distribution of the input point cloud. Therefore, estimating these parameters is challenging. This paper proposed a new version of DBSCAN that can automatically customize the parameters. The proposed method consists of two processes: initial parameter estimation based on grid analysis and DBSCAN based on the divide-and-conquer (DC-DBSCAN) approach, which repeatedly performs DBSCAN on each cluster separately and recursively. To verify the proposed method, we applied it to a 3D point cloud dataset that was used to analyze rockfall events at the Puiggcercos cliff, Spain. The total number of data points used in this study was 15,567. The experimental results show that the proposed method is better than the traditional DBSCAN in terms of purity and NMI scores. The purity scores of the proposed method and the traditional DBSCAN method were 96.22% and 91.09%, respectively. The NMI scores of the proposed method and the traditional DBSCAN method are 0.78 and 0.49, respectively. Also, it can detect events that traditional DBSCAN cannot detect.

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