Algorithms for Generating Adaptive Projection Patterns for 3D Shape Measurement

2008 ◽  
Vol 8 (3) ◽  
Author(s):  
Tao Peng ◽  
Satyandra K. Gupta
Keyword(s):  
Point Cloud ◽  
Measurement Accuracy ◽  
Point Clouds ◽  
3D Shape Measurement ◽  
Digital Fringe Projection ◽  
Physical Experiments ◽  
Dense Point ◽  
Improved Performance ◽  
Fringe Patterns ◽  
3D Shapes

Point cloud construction using digital fringe projection (PCCDFP) is a noncontact technique for acquiring dense point clouds to represent the 3D shapes of objects. Most existing PCCDFP systems use projection patterns consisting of straight fringes with fixed fringe pitches. In certain situations, such patterns do not give the best results. In our earlier work, we have shown that for surfaces with large range of normal directions, patterns that use curved fringes with spatial pitch variation can significantly improve the process of constructing point clouds. This paper describes algorithms for automatically generating adaptive projection patterns that use curved fringes with spatial pitch variation to provide improved results for an object being measured. We also describe the supporting algorithms that are needed for utilizing adaptive projection patterns. Both simulation and physical experiments show that adaptive patterns are able to achieve improved performance, in terms of measurement accuracy and coverage, as compared to fixed-pitch straight fringe patterns.

Algorithms for Generating Adaptive Projection Patterns for 3-D Shape Measurement

2007 ◽  
Author(s):  
Tao Peng ◽  
Satyandra K. Gupta
Keyword(s):  
Point Cloud ◽  
Measurement Accuracy ◽  
Point Clouds ◽  
Fringe Projection ◽  
Shape Measurement ◽  
Digital Fringe Projection ◽  
Physical Experiments ◽  
Dense Point ◽  
Improved Performance ◽  
Fringe Patterns

Point cloud acquisition using digital fringe projection (PCCDFP) is a non-contact technique for acquiring dense point clouds to represent the 3-D shapes of objects. Most existing PCCDFP systems use projection patterns consisting of straight fringes with fixed fringe pitches. In certain situations, such patterns do not give the best results. In our earlier work, we have shown that in some situations, patterns that use curved fringes with spatial pitch variation can significantly improve the process of constructing point clouds. This paper describes algorithms for automatically generating adaptive projection patterns that use curved fringes with spatial pitch variation to provide improved results for an object being measured. In addition, we also describe the supporting algorithms that are needed for utilizing adaptive projection patterns. Both simulation and physical experiments show that, adaptive patterns are able to achieve improved performance, in terms of measurement accuracy and coverage, than fixed-pitch straight fringe patterns.

scholarly journals Learning the Incremental Warp for 3D Vehicle Tracking in LiDAR Point Clouds

2021 ◽  
Vol 13 (14) ◽  
pp. 2770
Author(s):  
Shengjing Tian ◽  
Xiuping Liu ◽  
Meng Liu ◽  
Yuhao Bian ◽  
Junbin Gao ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Vehicle Tracking ◽  
Complex Nature ◽  
Global Feature ◽  
Tracking Accuracy ◽  
State Estimator ◽  
Latent Space ◽  
3D Shapes ◽  
Cloud Tracking

Object tracking from LiDAR point clouds, which are always incomplete, sparse, and unstructured, plays a crucial role in urban navigation. Some existing methods utilize a learned similarity network for locating the target, immensely limiting the advancements in tracking accuracy. In this study, we leveraged a powerful target discriminator and an accurate state estimator to robustly track target objects in challenging point cloud scenarios. Considering the complex nature of estimating the state, we extended the traditional Lucas and Kanade (LK) algorithm to 3D point cloud tracking. Specifically, we propose a state estimation subnetwork that aims to learn the incremental warp for updating the coarse target state. Moreover, to obtain a coarse state, we present a simple yet efficient discrimination subnetwork. It can project 3D shapes into a more discriminatory latent space by integrating the global feature into each point-wise feature. Experiments on KITTI and PandaSet datasets showed that compared with the most advanced of other methods, our proposed method can achieve significant improvements—in particular, up to 13.68% on KITTI.

scholarly journals MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Image

2019 ◽  
Vol 33 ◽  
pp. 8949-8956 ◽  
Author(s):  
Jinglu Wang ◽  
Bo Sun ◽  
Yan Lu
Keyword(s):  
Point Cloud ◽  
Image Plane ◽  
Point Clouds ◽  
Projective Planes ◽  
Network Architectures ◽  
View Point ◽  
Single Image ◽  
Dense Point ◽  
Dependent Point ◽  
3D Surfaces

In this paper, we address the problem of reconstructing an object’s surface from a single image using generative networks. First, we represent a 3D surface with an aggregation of dense point clouds from multiple views. Each point cloud is embedded in a regular 2D grid aligned on an image plane of a viewpoint, making the point cloud convolution-favored and ordered so as to fit into deep network architectures. The point clouds can be easily triangulated by exploiting connectivities of the 2D grids to form mesh-based surfaces. Second, we propose an encoder-decoder network that generates such kind of multiple view-dependent point clouds from a single image by regressing their 3D coordinates and visibilities. We also introduce a novel geometric loss that is able to interpret discrepancy over 3D surfaces as opposed to 2D projective planes, resorting to the surface discretization on the constructed meshes. We demonstrate that the multi-view point regression network outperforms state-of-the-art methods with a significant improvement on challenging datasets.

scholarly journals A comparison of multi-view 3D reconstruction of a rock wall using several cameras and a laser scanner

2014 ◽  
Vol XL-5 ◽  
pp. 573-580 ◽  
Author(s):  
K. Thoeni ◽  
A. Giacomini ◽  
R. Murtagh ◽  
E. Kniest
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Laser Scanner ◽  
Ground Truth ◽  
Point Clouds ◽  
Digital Cameras ◽  
Rock Wall ◽  
Geological Features ◽  
Dense Point ◽  
Sharp Edges

This work presents a comparative study between multi-view 3D reconstruction using various digital cameras and a terrestrial laser scanner (TLS). Five different digital cameras were used in order to estimate the limits related to the camera type and to establish the minimum camera requirements to obtain comparable results to the ones of the TLS. The cameras used for this study range from commercial grade to professional grade and included a GoPro Hero 1080 (5 Mp), iPhone 4S (8 Mp), Panasonic Lumix LX5 (9.5 Mp), Panasonic Lumix ZS20 (14.1 Mp) and Canon EOS 7D (18 Mp). The TLS used for this work was a FARO Focus 3D laser scanner with a range accuracy of ±2 mm. The study area is a small rock wall of about 6 m height and 20 m length. The wall is partly smooth with some evident geological features, such as non-persistent joints and sharp edges. Eight control points were placed on the wall and their coordinates were measured by using a total station. These coordinates were then used to georeference all models. A similar number of images was acquired from a distance of between approximately 5 to 10 m, depending on field of view of each camera. The commercial software package PhotoScan was used to process the images, georeference and scale the models, and to generate the dense point clouds. Finally, the open-source package CloudCompare was used to assess the accuracy of the multi-view results. Each point cloud obtained from a specific camera was compared to the point cloud obtained with the TLS. The latter is taken as ground truth. The result is a coloured point cloud for each camera showing the deviation in relation to the TLS data. The main goal of this study is to quantify the quality of the multi-view 3D reconstruction results obtained with various cameras as objectively as possible and to evaluate its applicability to geotechnical problems.

scholarly journals TERRESTRIAL PHOTOGRAMMETRY VS LASER SCANNING FOR RAPID EARTHQUAKE DAMAGE ASSESSMENT

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 527-533 ◽  
Author(s):  
C. Vasilakos ◽  
S. Chatzistamatis ◽  
O. Roussou ◽  
N. Soulakellis
Keyword(s):  
Structure From Motion ◽  
Damage Assessment ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Earthquake Damage ◽  
Map Building ◽  
Dense Point ◽  
Terrestrial Photogrammetry ◽  
Earthquake Damage Assessment

<p><strong>Abstract.</strong> Building damage assessment caused by earthquakes is essential during the response phase following a catastrophic event. Modern techniques include terrestrial and aerial photogrammetry based on Structure from Motion algorithm and Laser Scanning with the latter to prove its superiority in accuracy assessment due to the high-density point clouds. However, standardized procedures during emergency surveys often could not be followed due to restrictions of outdoor operations because of debris or decrepit buildings, the high human presence of civil protection agencies, expedited deployment of survey team and cost of operations. The aim of this paper is to evaluate whether terrestrial photogrammetry based on a handheld amateur DSLR camera can be used to map building damages, structural deformations and facade production in an accepted accuracy comparing to laser scanning technique. The study area is the Vrisa village, Lesvos, Greece where a Mw&amp;thinsp;6.3 earthquake occurred on June 12th, 2017. A dense point cloud from some digital images created based on Structure from Motion algorithm and compared with a dense point cloud acquired by a laser scanner. The distance measurement and the comparison were conducted with the Multiscale Model to Model Cloud Comparison method. According to the results, the mean of the absolute distances between the two clouds is 0.038&amp;thinsp;m while the 94.9&amp;thinsp;% of the point distances are less than 0.1&amp;thinsp;m. Terrestrial photogrammetry proved to be an accurate methodology for rapid earthquake damage assessment thus its products were used by local authorities for the calculation of the compensation for the property loss.</p>

scholarly journals A REGISTRATION METHOD OF POINT CLOUDS COLLECTED BY MOBILE LIDAR USING SOLELY STANDARD LAS FILES INFORMATION

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 121-128 ◽  
Author(s):  
L. Gézero ◽  
C. Antunes
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Auxiliary Information ◽  
Point Clouds ◽  
Registration Method ◽  
Digital Terrain ◽  
Terrain Models ◽  
Dense Point ◽  
Fully Automatic ◽  
Method Point

In the last few years, LiDAR sensors installed in terrestrial vehicles have been revealed as an efficient method to collect very dense 3D georeferenced information. The possibility of creating very dense point clouds representing the surface surrounding the sensor, at a given moment, in a very fast, detailed and easy way, shows the potential of this technology to be used for cartography and digital terrain models production in large scale. However, there are still some limitations associated with the use of this technology. When several acquisitions of the same area with the same device, are made, differences between the clouds can be observed. The range of that differences can go from few centimetres to some several tens of centimetres, mainly in urban and high vegetation areas where the occultation of the GNSS system introduces a degradation of the georeferenced trajectory. Along this article a different method point cloud registration is proposed. In addition to the efficiency and speed of execution, the main advantages of the method are related to the fact that the adjustment is continuously made over the trajectory, based on the GPS time. The process is fully automatic and only information recorded in the standard LAS files is used, without the need for any auxiliary information, in particular regarding the trajectory.

scholarly journals 3D THERMAL MAPPING OF BUILDING ROOFS BASED ON FUSION OF THERMAL AND VISIBLE POINT CLOUDS IN UAV IMAGERY

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 271-277
Author(s):  
M. Dahaghin ◽  
F. Samadzadegan ◽  
F. Dadras Javan
Keyword(s):  
Camera Calibration ◽  
Point Cloud ◽  
Point Clouds ◽  
Control Points ◽  
Thermal Camera ◽  
Dense Point ◽  
Normal Vectors ◽  
Projection Error ◽  
Orientation Parameters ◽  
Vegetation Layer

Abstract. Thermography is a robust method for detecting thermal irregularities on the roof of the buildings as one of the main energy dissipation parts. Recently, UAVs are presented to be useful in gathering 3D thermal data of the building roofs. In this topic, the low spatial resolution of thermal imagery is a challenge which leads to a sparse resolution in point clouds. This paper suggests the fusion of visible and thermal point clouds to generate a high-resolution thermal point cloud of the building roofs. For the purpose, camera calibration is performed to obtain internal orientation parameters, and then thermal point clouds and visible point clouds are generated. In the next step, both two point clouds are geo-referenced by control points. To extract building roofs from the visible point cloud, CSF ground filtering is applied, and the vegetation layer is removed by RGBVI index. Afterward, a predefined threshold is applied to the normal vectors in the z-direction in order to separate facets of roofs from the walls. Finally, the visible point cloud of the building roofs and registered thermal point cloud are combined and generate a fused dense point cloud. Results show mean re-projection error of 0.31 pixels for thermal camera calibration and mean absolute distance of 0.2 m for point clouds registration. The final product is a fused point cloud, which its density improves up to twice of the initial thermal point cloud density and it has the spatial accuracy of visible point cloud along with thermal information of the building roofs.

scholarly journals INVESTIGATIONS INTO THE QUALITY OF IMAGE-BASED POINT CLOUDS FROM UAV IMAGERY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 539-545 ◽  
Author(s):  
H.-J. Przybilla ◽  
M. Lindstaedt ◽  
T. Kersten
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Image Data ◽  
Point Clouds ◽  
Data Sets ◽  
Test Field ◽  
Data Format ◽  
Software Packages ◽  
Dense Point

<p><strong>Abstract.</strong> The quality of image-based point clouds generated from images of UAV aerial flights is subject to various influencing factors. In addition to the performance of the sensor used (a digital camera), the image data format (e.g. TIF or JPG) is another important quality parameter. At the UAV test field at the former Zollern colliery (Dortmund, Germany), set up by Bochum University of Applied Sciences, a medium-format camera from Phase One (IXU 1000) was used to capture UAV image data in RAW format. This investigation aims at evaluating the influence of the image data format on point clouds generated by a Dense Image Matching process. Furthermore, the effects of different data filters, which are part of the evaluation programs, were considered. The processing was carried out with two software packages from Agisoft and Pix4D on the basis of both generated TIF or JPG data sets. The point clouds generated are the basis for the investigation presented in this contribution. Point cloud comparisons with reference data from terrestrial laser scanning were performed on selected test areas representing object-typical surfaces (with varying surface structures). In addition to these area-based comparisons, selected linear objects (profiles) were evaluated between the different data sets. Furthermore, height point deviations from the dense point clouds were determined using check points. Differences in the results generated through the two software packages used could be detected. The reasons for these differences are filtering settings used for the generation of dense point clouds. It can also be assumed that there are differences in the algorithms for point cloud generation which are implemented in the two software packages. The slightly compressed JPG image data used for the point cloud generation did not show any significant changes in the quality of the examined point clouds compared to the uncompressed TIF data sets.</p>

scholarly journals Morphing and Sampling Network for Dense Point Cloud Completion

2020 ◽  
Vol 34 (07) ◽  
pp. 11596-11603 ◽  
Author(s):  
Minghua Liu ◽  
Lu Sheng ◽  
Sheng Yang ◽  
Jing Shao ◽  
Shi-Min Hu
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Coarse Grained ◽  
Second Stage ◽  
The Earth ◽  
Novel Approach ◽  
Dense Point ◽  
Chamfer Distance ◽  
Structural Loss ◽  
Two Stages

3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community. For acquiring high-fidelity dense point clouds and avoiding uneven distribution, blurred details, or structural loss of existing methods' results, we propose a novel approach to complete the partial point cloud in two stages. Specifically, in the first stage, the approach predicts a complete but coarse-grained point cloud with a collection of parametric surface elements. Then, in the second stage, it merges the coarse-grained prediction with the input point cloud by a novel sampling algorithm. Our method utilizes a joint loss function to guide the distribution of the points. Extensive experiments verify the effectiveness of our method and demonstrate that it outperforms the existing methods in both the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).

scholarly journals Effects of LiDAR Point Density on Extraction of Traffic Signs: A Sensitivity Study

2019 ◽  
Vol 2673 (1) ◽  
pp. 41-51 ◽  
Author(s):  
Suliman Gargoum ◽  
Karim El-Basyouny
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Sensitivity Study ◽  
Angle Of Incidence ◽  
Traffic Signs ◽  
Detection Rates ◽  
Point Density ◽  
Dense Point ◽  
Different Levels ◽  
Cloud Density

Datasets collected using light detection and ranging (LiDAR) technology often consist of dense point clouds. However, the density of the point cloud could vary depending on several different factors including the capabilities of the data collection equipment, the conditions in which data are collected, and other features such as range and angle of incidence. Although variation in point density is expected to influence the quality of the information extracted from LiDAR, the extent to which changes in density could affect the extraction is unknown. Understanding such impacts is essential for agencies looking to adopt LiDAR technology and researchers looking to develop algorithms to extract information from LiDAR. This paper focuses specifically on understanding the impacts of point density on extracting traffic signs from LiDAR datasets. The densities of point clouds are first reduced using stratified random sampling; traffic signs are then extracted from those datasets at different levels of point density. The precision and accuracy of the detection process was assessed at the different levels of point cloud density and on four different highway segments. In general, it was found that for signs with large panels along the approach on which LiDAR data were collected, reducing the point cloud density by up to 70% of the original point cloud had minimal impacts on the sign detection rates. Results of this study provide practical guidance to transportation agencies interested in understanding the tradeoff in price, quality, and coverage, when acquiring LiDAR equipment for the inventory of traffic signs on their transportation networks.

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