scholarly journals Sparse Regularization-Based Approach for Point Cloud Denoising and Sharp Features Enhancement

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3206
Author(s):  
Esmeide Leal ◽  
German Sanchez-Torres ◽  
John W. Branch
Keyword(s):  
Point Cloud ◽  
Median Filter ◽  
Smooth Point ◽  
Point Clouds ◽  
3D Models ◽  
L1 Norm ◽  
Sparse Regularization ◽  
Gradient Descent Algorithm ◽  
Proximal Gradient Descent ◽  
L1 Median

Denoising the point cloud is fundamental for reconstructing high quality surfaces with details in order to eliminate noise and outliers in the 3D scanning process. The challenges for a denoising algorithm are noise reduction and sharp features preservation. In this paper, we present a new model to reconstruct and smooth point clouds that combine L1-median filtering with sparse L1 regularization for both denoising the normal vectors and updating the position of the points to preserve sharp features in the point cloud. The L1-median filter is robust to outliers and noise compared to the mean. The L1 norm is a way to measure the sparsity of a solution, and applying an L1 optimization to the point cloud can measure the sparsity of sharp features, producing clean point set surfaces with sharp features. We optimize the L1 minimization problem by using the proximal gradient descent algorithm. Experimental results show that our approach is comparable to the state-of-the-art methods, as it filters out 3D models with a high level of noise, but keeps their geometric features.

scholarly journals DIRECT CO-REGISTRATION OF TIR IMAGES AND MLS POINT CLOUDS BY CORRESPONDING KEYPOINTS

2019 ◽  
Vol IV-2/W7 ◽  
pp. 235-242
Author(s):  
J. Zhu ◽  
Y. Xu ◽  
L. Hoegner ◽  
U. Stilla
Keyword(s):  
Condition Monitoring ◽  
Point Cloud ◽  
Infrared Image ◽  
Point Clouds ◽  
Thermal Infrared ◽  
3D Models ◽  
Corner Detection ◽  
Leakage Detection ◽  
Registration Process ◽  
Camera Pose

<p><strong>Abstract.</strong> In this work, we discussed how to directly combine thermal infrared image (TIR) and the point cloud without additional assistance from GCPs or 3D models. Specifically, we propose a point-based co-registration process for combining the TIR image and the point cloud for the buildings. The keypoints are extracted from images and point clouds via primitive segmentation and corner detection, then pairs of corresponding points are identified manually. After that, the estimated camera pose can be computed with EPnP algorithm. Finally, the point cloud with thermal information provided by IR images can be generated as a result, which is helpful in the tasks such as energy inspection, leakage detection, and abnormal condition monitoring. This paper provides us more insight about the probability and ideas about the combining TIR image and point cloud.</p>

scholarly journals ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-2 ◽  
pp. 797-804 ◽  
Author(s):  
W. Ostrowski ◽  
M. Pilarska ◽  
J. Charyton ◽  
K. Bakuła
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Statistical Parameters ◽  
Airborne Laser ◽  
Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

scholarly journals Accuracy assessment of building point clouds automatically generated from iphone images

2014 ◽  
Vol XL-5 ◽  
pp. 547-552 ◽  
Author(s):  
B. Sirmacek ◽  
R. Lindenbergh
Keyword(s):  
Measurement Errors ◽  
Point Cloud ◽  
Model Updating ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Point Clouds ◽  
3D Models ◽  
Map Updating ◽  
Cloud Points ◽  
Point To Point

Low-cost sensor generated 3D models can be useful for quick 3D urban model updating, yet the quality of the models is questionable. In this article, we evaluate the reliability of an automatic point cloud generation method using multi-view iPhone images or an iPhone video file as an input. We register such automatically generated point cloud on a TLS point cloud of the same object to discuss accuracy, advantages and limitations of the iPhone generated point clouds. For the chosen example showcase, we have classified 1.23% of the iPhone point cloud points as outliers, and calculated the mean of the point to point distances to the TLS point cloud as 0.11 m. Since a TLS point cloud might also include measurement errors and noise, we computed local noise values for the point clouds from both sources. Mean (μ) and standard deviation (&amp;sigma;) of roughness histograms are calculated as (μ<sub>1</sub> = 0.44 m., &amp;sigma;<sub>1</sub> = 0.071 m.) and (μ<sub>2</sub> = 0.025 m., &amp;sigma;<sub>2</sub> = 0.037 m.) for the iPhone and TLS point clouds respectively. Our experimental results indicate possible usage of the proposed automatic 3D model generation framework for 3D urban map updating, fusion and detail enhancing, quick and real-time change detection purposes. However, further insights should be obtained first on the circumstances that are needed to guarantee a successful point cloud generation from smartphone images.

scholarly journals THERMAL 3D MODELS ENHANCEMENT BASED ON INTEGRATION WITH VISIBLE IMAGERY

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 263-269
Author(s):  
F. Dadras Javan ◽  
M. Savadkouhi
Keyword(s):  
High Resolution ◽  
Thermal Imaging ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Models ◽  
Thermal Modelling ◽  
Surface Models ◽  
Geometric Quality ◽  
Imaging Sensors ◽  
Visible Imagery

Abstract. In the last few years, Unmanned Aerial Vehicles (UAVs) are being frequently used to acquire high resolution photogrammetric images and consequently producing Digital Surface Models (DSMs) and orthophotos in a photogrammetric procedure for topography and surface processing applications. Thermal imaging sensors are mostly used for interpretation and monitoring purposes because of lower geometric resolution. But yet, thermal mapping is getting more important in civil applications, as thermal sensors can be used in condition that visible sensors cannot, such as foggy weather and night times which is not possible for visible cameras. But, low geometric quality and resolution of thermal images is a main drawback that 3D thermal modelling are encountered with. This study aims to offer a solution for to fixing mentioned problem and generating a thermal 3D model with higher spatial resolution based on thermal and visible point clouds integration. This integration leads to generate a more accurate thermal point cloud and DEM with more density and resolution which is appropriate for 3D thermal modelling. The main steps of this study are: generating thermal and RGB point clouds separately, registration of them in two course and fine level and finally adding thermal information to RGB high resolution point cloud by interpolation concept. Experimental results are presented in a mesh that has more faces (With a factor of 23) which leads to a higher resolution textured mesh with thermal information.

scholarly journals A PROJECTION-BASED RECONSTRUCTION ALGORITHM FOR 3D MODELING OF BRIDGE STRUCTURES FROM DRONE-BASED POINT CLOUD

2021 ◽  
Vol XLVI-4/W1-2021 ◽  
pp. 77-83
Author(s):  
M. Mehranfar ◽  
H. Arefi ◽  
F. Alidoost
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
3D Model ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
3D Models ◽  
Reconstruction Technique ◽  
Reconstruction Process ◽  
Median Error ◽  
Bridge Structures

Abstract. This paper presents a projection-based method for 3D bridge modeling using dense point clouds generated from drone-based images. The proposed workflow consists of hierarchical steps including point cloud segmentation, modeling of individual elements, and merging of individual models to generate the final 3D model. First, a fuzzy clustering algorithm including the height values and geometrical-spectral features is employed to segment the input point cloud into the main bridge elements. In the next step, a 2D projection-based reconstruction technique is developed to generate a 2D model for each element. Next, the 3D models are reconstructed by extruding the 2D models orthogonally to the projection plane. Finally, the reconstruction process is completed by merging individual 3D models and forming an integrated 3D model of the bridge structure in a CAD format. The results demonstrate the effectiveness of the proposed method to generate 3D models automatically with a median error of about 0.025 m between the elements’ dimensions in the reference and reconstructed models for two different bridge datasets.

scholarly journals A Novel Preprocessing Method for Dynamic Point-Cloud Compression

2021 ◽  
Vol 11 (13) ◽  
pp. 5941
Author(s):  
Mun-yong Lee ◽  
Sang-ha Lee ◽  
Kye-dong Jung ◽  
Seung-hyun Lee ◽  
Soon-chul Kwon
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Point Cloud ◽  
High Performance ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
File Size ◽  
3D Point Clouds ◽  
Computer Based

Computer-based data processing capabilities have evolved to handle a lot of information. As such, the complexity of three-dimensional (3D) models (e.g., animations or real-time voxels) containing large volumes of information has increased exponentially. This rapid increase in complexity has led to problems with recording and transmission. In this study, we propose a method of efficiently managing and compressing animation information stored in the 3D point-clouds sequence. A compressed point-cloud is created by reconfiguring the points based on their voxels. Compared with the original point-cloud, noise caused by errors is removed, and a preprocessing procedure that achieves high performance in a redundant processing algorithm is proposed. The results of experiments and rendering demonstrate an average file-size reduction of 40% using the proposed algorithm. Moreover, 13% of the over-lap data are extracted and removed, and the file size is further reduced.

scholarly journals PHOTOGRAMMETRIC SOLUTIONS FOR 3D MODELING OF CULTURAL HERITAGE SITES IN REMOTE AREAS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 765-772
Author(s):  
A. Mostafavi ◽  
M. Scaioni ◽  
V. Yordanov
Keyword(s):  
Cultural Heritage ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Models ◽  
Digital Cameras ◽  
Minimal Cost ◽  
Remote Areas ◽  
Heritage Sites ◽  
3D Point Clouds ◽  
Realistic Possibility

Abstract. The realistic possibility of using non-metric digital cameras to achieve reliable 3D models has eased the application of photogrammetry in different domains. Documentation, conservation and dissemination of the Cultural Heritage (CH) can be obtained and implemented through virtual copies and replicas. Structure-from-Motion (SfM) photogrammetry has widely proven its impressive potential for image-based 3D reconstruction resulting in great 3D point clouds’ acquisitions but at minimal cost. Images from Unmanned Aerial Vehicles (UAVs) can be also processed within SfM pipeline to obtain point cloud of Cultural Heritage sites in remote regions. Both aerial and terrestrial images can be integrated to obtain a more complete 3D. In this paper, the application of SfM photogrammetry for surveying of the Ziggurat Chogha Zanbil in Iran is presented. Here point clouds have been derived from oblique and nadir photos captured from UAV as well as terrestrial photos. The obtained four point clouds have been compared on the basis of different techniques to highlight differences among them.

scholarly journals VAULT MODELING WITH NEURAL NETWORKS

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 81-86
Author(s):  
L. Barazzetti ◽  
M. Previtali
Keyword(s):  
Neural Networks ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
3D Models ◽  
Information Modeling ◽  
Free Form ◽  
Mathematical Representation ◽  
Mathematical Functions ◽  
Digital Reconstruction

<p><strong>Abstract.</strong> Nowadays, the digital reconstruction of vaults is carried out using photogrammetric and laser scanning techniques able to capture the visible surface with dense point clouds. Then, different modeling strategies allow the generation of 3D models in various formats, such as meshes that interpolates the acquired point cloud, NURBS-based reconstructions based on manual, semi-automated, or automated procedures, and parametric objects for Building Information Modeling. This paper proposes a novel method that reconstructs the visible surface of a vault using neural networks. It is based on the assumption that vaults are not irregular free-form objects, but they can be reconstructed by mathematical functions calculated from the acquired point clouds. The proposed approach uses the point cloud to train a neural network that approximates vault surface. The achieved solution is not only able to consider the basic geometry of the vault, but also its irregularities that cannot be neglected in the case of accurate and detailed modeling projects of historical vaults. Considerations on the approximation capabilities of neural networks are illustrated and discussed along with the advantages of creating a mathematical representation encapsulated into a function.</p>

scholarly journals A STOCHASTIC APPROACH TO AUTOMATED RECONSTRUCTION OF 3D MODELS OF INTERIOR SPACES FROM POINT CLOUDS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 299-306 ◽  
Author(s):  
H. Tran ◽  
K. Khoshelham
Keyword(s):  
Point Cloud ◽  
Input Data ◽  
Point Clouds ◽  
Stochastic Approach ◽  
3D Models ◽  
Lidar Data ◽  
Interior Spaces ◽  
Wide Range ◽  
3D Shapes ◽  
Modelling Process

<p><strong>Abstract.</strong> Automated reconstruction of 3D interior models has recently been a topic of intensive research due to its wide range of applications in Architecture, Engineering, and Construction. However, generation of the 3D models from LiDAR data and/or RGB-D data is challenged by not only the complexity of building geometries, but also the presence of clutters and the inevitable defects of the input data. In this paper, we propose a stochastic approach for automatic reconstruction of 3D models of interior spaces from point clouds, which is applicable to both Manhattan and non-Manhattan world buildings. The building interior is first partitioned into a set of 3D shapes as an arrangement of permanent structures. An optimization process is then applied to search for the most probable model as the optimal configuration of the 3D shapes using the reversible jump Markov Chain Monte Carlo (rjMCMC) sampling with the Metropolis-Hastings algorithm. This optimization is not based only on the input data, but also takes into account the intermediate stages of the model during the modelling process. Consequently, it enhances the robustness of the proposed approach to inaccuracy and incompleteness of the point cloud. The feasibility of the proposed approach is evaluated on a synthetic and an ISPRS benchmark dataset.</p>

scholarly journals Parametric Point Cloud Slicing for Facade Retrofitting

2021 ◽  
Author(s):  
Oscar Gámez Bohórquez ◽  
William Derigent ◽  
Hind Bril El Haouzi
Keyword(s):  
Measurement Errors ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Building Envelope ◽  
Parametric Modelling ◽  
Building Stock ◽  
File Formats ◽  
Modelling Environment

Current commitments by European governments seek to improve energy consumption as a means to reduce carbon emissions from building stock by 2050. Within such context, retrieving reliable three-dimensional contours from point clouds becomes an important step in developing facade retrofitting solutions since facade retrofitting projects often make use of as-built 3D models to help reduce inaccuracies by narrowing interpretation and measurement errors. This work aims to provide a method that uses topology-based parametric modelling for reconstructing building envelopes from point clouds. Through a semi-automated process that gives permanent visual feedback, the user adjusts parameters to custom standards of acceptability. A solution under the form of a Grasshopper definition delivers building envelope 3D contours in various file formats as a means for increasing interoperability. The main contributions of this work consist of a parametric reconstruction workflow capable of solving building topology for retrieving 3D contours, a strategy to bypass point cloud occlusion, and a strategy for converting those contours into an IFC model directly from the parametric modelling environment.

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