Processing and interactive editing of huge point clouds from 3D scanners

2008 ◽  
Vol 32 (2) ◽  
pp. 204-220 ◽  
Author(s):  
Michael Wand ◽  
Alexander Berner ◽  
Martin Bokeloh ◽  
Philipp Jenke ◽  
Arno Fleck ◽  
...  
Keyword(s):  
Point Clouds ◽  
Interactive Editing ◽  
3D Scanners

scholarly journals Recognition of Point Sets Objects in Realistic Scenes

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ruizhen Gao ◽  
Xiaohui Li ◽  
Jingjun Zhang
Keyword(s):  
Network Structure ◽  
Point Cloud ◽  
Euclidean Distance ◽  
Three Dimensional ◽  
Point Clouds ◽  
Accuracy Rate ◽  
Cloud Data ◽  
3D Scanners ◽  
Deep Learning Network ◽  
Realistic Scenes

With the emergence of new intelligent sensing technologies such as 3D scanners and stereo vision, high-quality point clouds have become very convenient and lower cost. The research of 3D object recognition based on point clouds has also received widespread attention. Point clouds are an important type of geometric data structure. Because of its irregular format, many researchers convert this data into regular three-dimensional voxel grids or image collections. However, this can lead to unnecessary bulk of data and cause problems. In this paper, we consider the problem of recognizing objects in realistic senses. We first use Euclidean distance clustering method to segment objects in realistic scenes. Then we use a deep learning network structure to directly extract features of the point cloud data to recognize the objects. Theoretically, this network structure shows strong performance. In experiment, there is an accuracy rate of 98.8% on the training set, and the accuracy rate in the experimental test set can reach 89.7%. The experimental results show that the network structure in this paper can accurately identify and classify point cloud objects in realistic scenes and maintain a certain accuracy when the number of point clouds is small, which is very robust.

scholarly journals KINECT V2 AND RGB STEREO CAMERAS INTEGRATION FOR DEPTH MAP ENHANCEMENT

2016 ◽  
Vol XLI-B5 ◽  
pp. 699-702 ◽  
Author(s):  
R. Ravanelli ◽  
A. Nascetti ◽  
M. Crespi
Keyword(s):  
Low Cost ◽  
Depth Map ◽  
Point Clouds ◽  
Quality Standard ◽  
Frame Rate ◽  
Stereo Pair ◽  
Kinect V2 ◽  
Dense Point ◽  
3D Scanners ◽  
Modelling Techniques

Today range cameras are widespread low-cost sensors based on two different principles of operation: we can distinguish between Structured Light (SL) range cameras (Kinect v1, Structure Sensor, ...) and Time Of Flight (ToF) range cameras (Kinect v2, ...). Both the types are easy to use 3D scanners, able to reconstruct dense point clouds at high frame rate. However the depth maps obtained are often noisy and not enough accurate, therefore it is generally essential to improve their quality. Standard RGB cameras can be a valuable solution to solve such issue. The aim of this paper is therefore to evaluate the integration feasibility of these two different 3D modelling techniques, characterized by complementary features and based on standard low-cost sensors. <br><br> For this purpose, a 3D model of a DUPLO<sup>TM</sup> bricks construction was reconstructed both with the Kinect v2 range camera and by processing one stereo pair acquired with a Canon Eos 1200D DSLR camera. The scale of the photgrammetric model was retrieved from the coordinates measured by Kinect v2. The preliminary results are encouraging and show that the foreseen integration could lead to an higher metric accuracy and a major level of completeness with respect to that obtained by using only separated techniques.

scholarly journals Improving Completeness of Geometric Models from Terrestrial Laser Scanning Data

2011 ◽  
Vol 6 ◽  
pp. 233-240 ◽  
Author(s):  
Clemens Nothegger
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
World Heritage Site ◽  
Alternative Methods ◽  
Interactive Editing ◽  
Unesco World Heritage Site ◽  
Topological Errors ◽  
Scan Data

The application of terrestrial laser scanning for the documentation of cultural heritage assets is becoming increasingly common. While the point cloud by itself is sufficient for satisfying many documentation needs, it is often desirable to use this data for applications other than documentation. For these purposes a triangulated model is usually required. The generation of topologically correct triangulated models from terrestrial laser scans, however, still requires much interactive editing. This is especially true when reconstructing models from medium range panoramic scanners and many scan positions. Because of residual errors in the instrument calibration and the limited spatial resolution due to the laser footprint, the point clouds from different scan positions never match perfectly. Under these circumstances many of the software packages commonly used for generating triangulated models produce models which have topological errors such as surface intersecting triangles, holes or triangles which violate the manifold property. We present an algorithm which significantly reduces the number of topological errors in the models from such data. The algorithm is a modification of the Poisson surface reconstruction algorithm. Poisson surfaces are resilient to noise in the data and the algorithm always produces a closed manifold surface. Our modified algorithm partitions the data into tiles and can thus be easily parallelized. Furthermore, it avoids introducing topological errors in occluded areas, albeit at the cost of producing models which are no longer guaranteed to be closed. The algorithm is applied to scan data of sculptures of the UNESCO World Heritage Site Schönbrunn Palace and data of a petrified oyster reef in Stetten, Austria. The results of the method’s application are discussed and compared with those of alternative methods.

Error Propagation in Digital Additive Remanufacturing Process Planning

2019 ◽  
Author(s):  
Mojahed Alkhateeb ◽  
Jeremy L. Rickli ◽  
Nicholas J. Christoforou
Keyword(s):  
Error Propagation ◽  
Point Cloud ◽  
Point Clouds ◽  
Propagation Model ◽  
Smoothing Factor ◽  
Material Deposition ◽  
Proposed Model ◽  
Data Points ◽  
3D Scanners ◽  
The Right

Abstract A point cloud is a digital representation of a part that consists of a set of data points in space. Typically point clouds are produced by 3D scanners that hover above a part and records points in a large number that represent the external surface of a part. Additive remanufacturing offers a sustainable solution to end-of-use (EoU) core disposal and recovery and requires quantification of part damage or wear that requires reprocessing. This paper proposes an error propagation approach that models the interaction of each step of the additive remanufacturing process. This proposed model is formulated, and the results of the errors generated from the parameters of the scanner and point cloud smoothing are presented. Smoothing is an important step to reduce the noises generated from scanning, knowing the right smoothing factor is important since over smoothing results in dimensional inaccuracies and errors, especially in cores with smaller degrees of damage. It is important to know the error generated from scanning and point cloud smoothing to compensate in the following steps and generate appropriate material deposition paths. Inaccuracies in the 3D model renders can impact the remainder of the additive remanufacturing accuracy, especially because there are multiple steps in the process. Sources of error from smoothing, meshing, slicing, and material deposition are proposed in the error propagation model for additive remanufacturing. Results of efforts to quantify the scanning and smoothing steps within this model are presented.

scholarly journals PERFORMANCE INVESTIGATION OF A HANDHELD 3D SCANNER TO DEFINE GOOD PRACTICES FOR SMALL ARTEFACT 3D MODELING

2017 ◽  
Vol XLII-2/W5 ◽  
pp. 427-434 ◽  
Author(s):  
E. Lachat ◽  
T. Landes ◽  
P. Grussenmeyer
Keyword(s):  
Large Scale ◽  
Accuracy Assessment ◽  
Specific Treatment ◽  
Point Clouds ◽  
Practical Case ◽  
3D Scanner ◽  
Scale Models ◽  
3D Scanners ◽  
Handheld Scanner

Handheld 3D scanners can be used to complete large scale models with the acquisition of occluded areas or small artefacts. This may be of interest for digitization projects in the field of Cultural Heritage, where detailed areas may require a specific treatment. Such sensors present the advantage of being easily portable in the field, and easily usable even without particular knowledge. In this paper, the Freestyle<sup>3D</sup> handheld scanner launched on the market in 2015 by FARO is investigated. Different experiments are described, covering various topics such as the influence of range or color on the measurements, but also the precision achieved for geometrical primitive digitization. These laboratory experiments are completed by acquisitions performed on engraved and sculpted stone blocks. This practical case study is useful to investigate which acquisition protocol seems to be the more adapted and leads to precise results. The produced point clouds will be compared to photogrammetric surveys for the purpose of their accuracy assessment.

scholarly journals Worm Selector: Volume Selection in a 3D Point Cloud Through Adaptive Modelling

2017 ◽  
Vol 17 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Emmanuel Dubois ◽  
Adrien Hamelin
Keyword(s):  
Point Cloud ◽  
User Study ◽  
Point Clouds ◽  
Ease Of Use ◽  
Free Form ◽  
3D Point Cloud ◽  
3D Point Clouds ◽  
3D Scanners ◽  
Definition Of ◽  
Straight Lines

3D point clouds are more and more widely used, especially because of the proliferation of manual and cheap 3D scanners and 3D printers. Due to the large size of the 3D point clouds, selecting part of them is very often required. Existing interaction techniques include ray/cone casting and predefined or free-form selection volume. In order to cope with the traditional trade-off between accuracy, ease of use and flexibility of these different forms of selection techniques in a 3D point cloud, we present the Worm Selector. It allows to select complex shapes while remaining simple to use and accurate. Using the Worm Selector relies on three principles: 1) points are selected by progressively constructing a cylinder-like shape (the adaptative worm) through the sequential definition of several sections; 2) a section is defined as a set of two contours linked together with straight lines; 3) each contour is a freely drawn closed shape. A user study reveals that the Worm Selector is significantly faster than a classical selection mechanism based on predefined volumes such as spheres or cuboids, while maintaining a comparable level of precision and recall.

Point Clouds Pre-Processing and Surface Reconstruction Based on Tangent Continuity Algorithm Technique

2020 ◽  
Vol 38 (6A) ◽  
pp. 917-925
Author(s):  
Ali M. Al-Bdairy ◽  
Ahmed A.A. Al-Duroobi ◽  
Maan A. Tawfiq
Keyword(s):  
Case Studies ◽  
Surface Reconstruction ◽  
Laser Scanner ◽  
Point Clouds ◽  
3D Objects ◽  
Data Points ◽  
3D Scanners ◽  
Different Shapes ◽  
Noise Factors

Pre-processing is essential for processing the row data point clouds which acquired using a 3D laser scanner as a modern technique to digitize and reconstruct the surface of the 3D objects in reverse engineering applications. Due to the accuracy limitation of some 3D scanners and the environmental noise factors such as illumination and reflection, there are some noised data points associated with the row point clouds, so, in the present paper, a preprocessing algorithm has been proposed to determine and delete the unnecessary data as noised points and save the remaining data points for the surface reconstruction of 3D objects from its point clouds which acquired using the 3D laser scanner (Matter and Form). The proposed algorithm based on the assessment of tangent continuity as a geometrical feature and criteria for the contiguous points. A MATLAB software has been used to construct a program for the proposed point clouds pre-processing algorithm, the validity of the constructed program has been proved using geometrical case studies with different shapes. The application results of the proposed tangent algorithm and surface fitting process for the suggested case studies were proved the validity of the proposed algorithm for simplification of the point clouds, where the percent of noised data which removed according to the proposed tangent continuity algorithm which achieved a reduction of the total points to a percentage of (43.63%), and (32.01%) for the studied case studies, from the total number of data points in point cloud for first and second case study respectively.

scholarly journals KINECT V2 AND RGB STEREO CAMERAS INTEGRATION FOR DEPTH MAP ENHANCEMENT

2016 ◽  
Vol XLI-B5 ◽  
pp. 699-702
Author(s):  
R. Ravanelli ◽  
A. Nascetti ◽  
M. Crespi
Keyword(s):  
Low Cost ◽  
Depth Map ◽  
Point Clouds ◽  
Quality Standard ◽  
Frame Rate ◽  
Stereo Pair ◽  
Kinect V2 ◽  
Dense Point ◽  
3D Scanners ◽  
Modelling Techniques

Today range cameras are widespread low-cost sensors based on two different principles of operation: we can distinguish between Structured Light (SL) range cameras (Kinect v1, Structure Sensor, ...) and Time Of Flight (ToF) range cameras (Kinect v2, ...). Both the types are easy to use 3D scanners, able to reconstruct dense point clouds at high frame rate. However the depth maps obtained are often noisy and not enough accurate, therefore it is generally essential to improve their quality. Standard RGB cameras can be a valuable solution to solve such issue. The aim of this paper is therefore to evaluate the integration feasibility of these two different 3D modelling techniques, characterized by complementary features and based on standard low-cost sensors. &lt;br&gt;&lt;br&gt; For this purpose, a 3D model of a DUPLO&lt;sup&gt;TM&lt;/sup&gt; bricks construction was reconstructed both with the Kinect v2 range camera and by processing one stereo pair acquired with a Canon Eos 1200D DSLR camera. The scale of the photgrammetric model was retrieved from the coordinates measured by Kinect v2. The preliminary results are encouraging and show that the foreseen integration could lead to an higher metric accuracy and a major level of completeness with respect to that obtained by using only separated techniques.

CAD Model Generation Using 3D Scanning

2007 ◽  
Vol 23 ◽  
pp. 169-172
Author(s):  
Khalil Khalili ◽  
Seyed Yousef Ahmadi-Brooghani ◽  
M. Rakhshkhorshid
Keyword(s):  
Programming Languages ◽  
Point Clouds ◽  
3D Scanning ◽  
Industrial Applications ◽  
Free Form ◽  
Depth Information ◽  
Fuzzy Logic Algorithm ◽  
3D Scanners ◽  
Processing Techniques ◽  
Free Form Surfaces

3D Scanners are used in industrial applications such as reverse engineering and inspection. Customization of existing CAD systems is one of rapid ways to supplying a 3D Scanning software. In this paper, using AutoLisp and Visual Basic programming languages, AutoCAD has been customized. Also facilities of automatic scanning of physical parts, in the domain of free form surfaces, have been provided. Furthermore, possibilities such as, control of scanner automotive system, representation of registered point clouds, generation of polygon and /or NURBS model from primary or modified point clouds, have been prepared. Triangulation and image processing techniques along with a new fuzzy logic algorithm have been used to extract the depth information more accurate. These, accompanying with AutoCAD capabilities have provided acceptable facilities for 3D scanning.

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