The Depth Map Construction from a 3D Point Cloud

Chmelar, Pavel; Beran, Ladislav; Rejfek, Lubos

doi:10.1051/matecconf/20167503005

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…8 shows the range view in front of the autonomous scanning system introduced in (Chmelar and Dobrovolny, 2013). This range view was used in our previous work, where we introduced the depth map construction from a 3D point cloud (Chmelar et al, 2016).…”

Section: Results Evaluation and Examples Of Usingmentioning

confidence: 99%

A point cloud decomposition by the 3D level scanning for planes detection

Chmelar

2017

Int. j. adv. appl. sci.

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A point cloud represents a set of measurement points. Usually it is a group of points in a defined coordinate space without any information how individual points relates to each other. For a simple shapes and objects description additional methods are needed. In this paper we would like to present a new 3D point cloud scanning method for planes detection. Our developed algorithm includes several image processing methods like the connected component labeling and the shape borders detection which allows computing important plane properties end even detect object shapes. The scanning algorithm is described on a testing example and verified on real measured data. The paper concludes by algorithm properties summarization and recommendations where this method can be used.

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Section: Results Evaluation and Examples Of Usingmentioning

confidence: 99%

A point cloud decomposition by the 3D level scanning for planes detection

Chmelar

2017

Int. j. adv. appl. sci.

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“…Then, using geometric arguments, we implement the process of converting the screen coordinate system to a Cartesian coordinate system. This idea was born from the reverse engineering results presented in [44]. However, despite obtaining a point cloud of the weld surface, it is worth recalling the geometry of the test object, the pipe, of which, as a result, the three-dimensional reconstruction should be a three-dimensional ring.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning and 3D Reconstruction of Materials Surface for Nondestructive Inspection

Kartashov

Чернов

Alexandrov

et al. 2022

Sensors

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During the steel pipeline installation, special attention is paid to the butt weld control performed by fusion welding. The operation of the currently popular automated X-ray and ultrasonic testing complexes is associated with high resource and monetary costs. In this regard, this work is devoted to the development of alternative and cost-effective means of preliminary quality control of the work performed based on the visual testing method. To achieve this goal, a hardware platform based on a single board Raspberry Pi4 minicomputer and a set of available modules and expansion cards is proposed, and software whose main functionality is implemented based on the systemic application of computer vision algorithms and machine learning methods. The YOLOv5 object detection algorithm and the random forest machine learning model were used as a defect detection and classification system. The mean average precision (mAP) of the trained YOLOv5 algorithm based on extracted weld contours is 86.9%. A copy of YOLOv5 trained on the images of control objects showed a mAP result of 96.8%. Random forest identifying of the defect precursor based on the point clouds of the weld surface achieved a mAP of 87.5%.

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“…The depth images contain the real physical sizes and distances which are possible to utilize further. We had a point cloud without depth information, so we decided to develop the algorithm for the depth map construction, which we described in reference [28]. The main advantage of this algorithm lies in the possibility to create a depth map from any point cloud with the arbitrary camera position and arbitrary camera orientation.…”

Section: Our Point Cloud Processing Contributionmentioning

confidence: 99%

Advanced Methods for Point Cloud Processing and Simplification

et al. 2020

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Nowadays, mobile robot exploration needs a rangefinder to obtain a large number of measurement points to achieve a detailed and precise description of a surrounding area and objects, which is called the point cloud. However, a single point cloud scan does not cover the whole area, so multiple point cloud scans must be acquired and compared together to find the right matching between them in a process called registration method. This method requires further processing and places high demands on memory consumption, especially for small embedded devices in mobile robots. This paper describes a novel method to reduce the burden of processing for multiple point cloud scans. We introduce our approach to preprocess an input point cloud in order to detect planar surfaces, simplify space description, fill gaps in point clouds, and get important space features. All of these processes are achieved by applying advanced image processing methods in combination with the quantization of physical space points. The results show the reliability of our approach to detect close parallel walls with suitable parameter settings. More importantly, planar surface detection shows a 99% decrease in necessary descriptive points almost in all cases. This proposed approach is verified on the real indoor point clouds.

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The Depth Map Construction from a 3D Point Cloud

Cited by 4 publications

References 9 publications

A point cloud decomposition by the 3D level scanning for planes detection

A point cloud decomposition by the 3D level scanning for planes detection

Machine Learning and 3D Reconstruction of Materials Surface for Nondestructive Inspection

Advanced Methods for Point Cloud Processing and Simplification

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