Evaluation of Geometric Quality of 3D Models Obtained Automatically by Robotic RevoScan Device

This paper examines the results of image enhancement and point cloud filtering on the visual and geometric quality of 3D models for the representation of underwater features. Specifically it evaluates the combination of effects from the manual editing of images’ radiometry (captured at shallow depths) and the selection of parameters for point cloud definition and mesh building (processed in 3D modeling software). Such datasets, are usually collected by divers, handled by scientists and used for geovisualization purposes. In the presented study, have been created 3D models from three sets of images (seafloor, part of a wreck and a small boat's wreck) captured at three different depths (3.5m, 10m and 14m respectively). Four models have been created from the first dataset (seafloor) in order to evaluate the results from the application of image enhancement techniques and point cloud filtering. The main process for this preliminary study included a) the definition of parameters for the point cloud filtering and the creation of a reference model, b) the radiometric editing of images, followed by the creation of three improved models and c) the assessment of results by comparing the visual and the geometric quality of improved models versus the reference one. Finally, the selected technique is tested on two other data sets in order to examine its appropriateness for different depths (at 10m and 14m) and different objects (part of a wreck and a small boat's wreck) in the context of an ongoing research in the Laboratory of Photogrammetry and Remote Sensing.

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UNDERWATER 3D MODELING: IMAGE ENHANCEMENT AND POINT CLOUD FILTERING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b2-441-2016 ◽

2016 ◽

Vol XLI-B2 ◽

pp. 441-447

Author(s):

I. Sarakinou ◽

K. Papadimitriou ◽

O. Georgoula ◽

P. Patias

Keyword(s):

Image Enhancement ◽

3D Modeling ◽

Point Cloud ◽

3D Models ◽

Main Process ◽

Ongoing Research ◽

Different Depths ◽

The Creation ◽

Geometric Quality

This paper examines the results of image enhancement and point cloud filtering on the visual and geometric quality of 3D models for the representation of underwater features. Specifically it evaluates the combination of effects from the manual editing of images’ radiometry (captured at shallow depths) and the selection of parameters for point cloud definition and mesh building (processed in 3D modeling software). Such datasets, are usually collected by divers, handled by scientists and used for geovisualization purposes. In the presented study, have been created 3D models from three sets of images (seafloor, part of a wreck and a small boat's wreck) captured at three different depths (3.5m, 10m and 14m respectively). Four models have been created from the first dataset (seafloor) in order to evaluate the results from the application of image enhancement techniques and point cloud filtering. The main process for this preliminary study included a) the definition of parameters for the point cloud filtering and the creation of a reference model, b) the radiometric editing of images, followed by the creation of three improved models and c) the assessment of results by comparing the visual and the geometric quality of improved models versus the reference one. Finally, the selected technique is tested on two other data sets in order to examine its appropriateness for different depths (at 10m and 14m) and different objects (part of a wreck and a small boat's wreck) in the context of an ongoing research in the Laboratory of Photogrammetry and Remote Sensing.

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Analysis of the influence of the hole depth on the geometric quality of the machined part, when the cutting fluid and the machining parameters are varied

10.26678/abcm.cobem2017.cob17-1834 ◽

2017 ◽

Author(s):

Eder Silva Costa ◽

Pedro Henrique Pires França ◽

Leonardo Rosa Ribeiro da Silva ◽

Wisley Sales ◽

Álisson Rocha Machado ◽

...

Keyword(s):

Cutting Fluid ◽

Machining Parameters ◽

Hole Depth ◽

Geometric Quality

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Application of 3D-printing technologies for production of master-model in engineering industry

Technology of metals ◽

10.31044/1684-2499-2021-0-9-17-21 ◽

2021 ◽

Vol 0 (9) ◽

pp. 17-21

Author(s):

O. A. Dvoryankin ◽

◽

N. I. Baurova ◽

Keyword(s):

3D Printing ◽

Layer Thickness ◽

Ultimate Strength ◽

3D Models ◽

Engineering Industry ◽

Master Model ◽

Parameter Influence ◽

Printing Parameter

Analysis of 3D-printing methods used in the molding production to manufacture master-models has been carried out. The technology was selected, which allowed one to make high-precision parts, combining the molding and the 3D-printing. Factors effecting on the quality of 3D-models printed by this technology were analyzed. Experimental studied for determination of the printing parameter influence (layer thickness, filling percentage, printing velocity) on ultimate strength of specimens made of ABS-plastic were carried out.

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A Proposal for the Standardized Reporting of Error and Paradata Regarding Structure from Motion (SfM) 3D Models Used in Recording and Consolidating Archaeological Architecture

Advances in Archaeological Practice ◽

10.1017/aap.2020.11 ◽

2020 ◽

Vol 8 (4) ◽

pp. 376-388

Author(s):

Mario Borrero ◽

Luke R. Stroth

Keyword(s):

Structure From Motion ◽

3D Models ◽

Model Accuracy ◽

Final State ◽

Final Model ◽

The Past ◽

Reprojection Error ◽

Standardized Reporting

AbstractIn the past decade, archaeologists have increasingly made use of photogrammetry, the process of creating 3D models from photographs, in a variety of field and lab settings. We argue that we must, as a discipline, develop a consistent methodology to ensure that 3D models are held to a consistent standard, including not only photographic protocol but also the documentation of model accuracy using an agreed-upon measure. To help develop this discussion, we present our system for incorporating photogrammetry into the documentation of architecture. This technique was developed at the site of Nim Li Punit, Belize, in 2018. Excavating architecture involves documenting the pre-excavated building, liberating overburden, documenting all in situ construction (including wall fall, fill stones, and standing architecture), drawing consolidated architecture, and documenting the final state of the post-excavated buildings. The generation of 3D models greatly assisted in all facets of the excavation, documentation, analysis, and consolidation processes. To ensure that our models were accurate, we documented the reprojection error and final model horizontal distortion to assess the quality of the model. We suggest that documenting both forms of error should become standard practice in any discussion of archaeological applications of photogrammetry.

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2D Image-To-3D Model: Knowledge-Based 3D Building Reconstruction (3DBR) Using Single Aerial Images and Convolutional Neural Networks (CNNs)

Remote Sensing ◽

10.3390/rs11192219 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2219 ◽

Cited By ~ 6

Author(s):

Fatemeh Alidoost ◽

Hossein Arefi ◽

Federico Tombari

Keyword(s):

Test Data ◽

3D Models ◽

Parametric Models ◽

Aerial Images ◽

Aerial Image ◽

Absolute Deviation ◽

Multi Scale ◽

Knowledge Based ◽

Linear Elements

In this study, a deep learning (DL)-based approach is proposed for the detection and reconstruction of buildings from a single aerial image. The pre-required knowledge to reconstruct the 3D shapes of buildings, including the height data as well as the linear elements of individual roofs, is derived from the RGB image using an optimized multi-scale convolutional–deconvolutional network (MSCDN). The proposed network is composed of two feature extraction levels to first predict the coarse features, and then automatically refine them. The predicted features include the normalized digital surface models (nDSMs) and linear elements of roofs in three classes of eave, ridge, and hip lines. Then, the prismatic models of buildings are generated by analyzing the eave lines. The parametric models of individual roofs are also reconstructed using the predicted ridge and hip lines. The experiments show that, even in the presence of noises in height values, the proposed method performs well on 3D reconstruction of buildings with different shapes and complexities. The average root mean square error (RMSE) and normalized median absolute deviation (NMAD) metrics are about 3.43 m and 1.13 m, respectively for the predicted nDSM. Moreover, the quality of the extracted linear elements is about 91.31% and 83.69% for the Potsdam and Zeebrugge test data, respectively. Unlike the state-of-the-art methods, the proposed approach does not need any additional or auxiliary data and employs a single image to reconstruct the 3D models of buildings with the competitive precision of about 1.2 m and 0.8 m for the horizontal and vertical RMSEs over the Potsdam data and about 3.9 m and 2.4 m over the Zeebrugge test data.

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Automated Geometric Quality Inspection of Prefabricated Housing Units Using BIM and LiDAR

Remote Sensing ◽

10.3390/rs12152492 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2492

Author(s):

Yi Tan ◽

Silin Li ◽

Qian Wang

Keyword(s):

Building Information Modeling ◽

Information Modeling ◽

The Other ◽

Light Detection And Ranging ◽

Quality Inspection ◽

Building Information ◽

Geometric Quality ◽

Mechanical Elements ◽

Housing Units

Traditional quality inspection of prefabricated components is labor intensive, time-consuming, and error prone. This study developed an automated geometric quality inspection technique for prefabricated housing units using building information modeling (BIM) and light detection and ranging (LiDAR). The proposed technique collects the 3D laser scanned data of the prefabricated unit using a LiDAR which contains accurate as-built surface geometries of the prefabricated unit. On the other hand, the BIM model of the prefabricated unit contains the as-designed geometries of the unit. The scanned data and BIM model are then automatically processed to inspect the geometric quality of individual elements of the prefabricated units including both structural and mechanical elements, as well as electrical and plumbing (MEP) elements. To validate the proposed technique, experiments were conducted on two prefabricated bathroom units (PBUs). The inspection results showed that the proposed technique can provide accurate quality inspection results with 0.7 mm and 0.9 mm accuracy for structural and MEP elements, respectively. In addition, the experiments also showed that the proposed technique greatly improves the inspection efficiency regarding time and labor.

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Spectral Processing for Denoising and Compression of 3D Meshes Using Dynamic Orthogonal Iterations

Journal of Imaging ◽

10.3390/jimaging6060055 ◽

2020 ◽

Vol 6 (6) ◽

pp. 55

Author(s):

Gerasimos Arvanitis ◽

Aris S. Lalos ◽

Konstantinos Moustakas

Keyword(s):

Computational Complexity ◽

Laplacian Matrix ◽

3D Models ◽

Graph Laplacian ◽

Fixed Number ◽

Spectral Processing ◽

Reconstruction Quality ◽

And Performance ◽

Optimal Subspace

Recently, spectral methods have been extensively used in the processing of 3D meshes. They usually take advantage of some unique properties that the eigenvalues and the eigenvectors of the decomposed Laplacian matrix have. However, despite their superior behavior and performance, they suffer from computational complexity, especially while the number of vertices of the model increases. In this work, we suggest the use of a fast and efficient spectral processing approach applied to dense static and dynamic 3D meshes, which can be ideally suited for real-time denoising and compression applications. To increase the computational efficiency of the method, we exploit potential spectral coherence between adjacent parts of a mesh and then we apply an orthogonal iteration approach for the tracking of the graph Laplacian eigenspaces. Additionally, we present a dynamic version that automatically identifies the optimal subspace size that satisfies a given reconstruction quality threshold. In this way, we overcome the problem of the perceptual distortions, due to the fixed number of subspace sizes that is used for all the separated parts individually. Extensive simulations carried out using different 3D models in different use cases (i.e., compression and denoising), showed that the proposed approach is very fast, especially in comparison with the SVD based spectral processing approaches, while at the same time the quality of the reconstructed models is of similar or even better reconstruction quality. The experimental analysis also showed that the proposed approach could also be used by other denoising methods as a preprocessing step, in order to optimize the reconstruction quality of their results and decrease their computational complexity since they need fewer iterations to converge.

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Visually Guided Object Insertion by Cooperative Robots in Unstructured Environments

Volume 2: 28th Biennial Mechanisms and Robotics Conference, Parts A and B ◽

10.1115/detc2004-57035 ◽

2004 ◽

Author(s):

Vivek A. Sujan

Keyword(s):

Information Content ◽

3D Models ◽

Visual Exploration ◽

Visually Guided ◽

Environment Model ◽

New Information ◽

Camera Position ◽

Scene Information ◽

And Task

In field environments it is not usually possible to provide robots in advance with valid geometric models of its environment and task element locations. The robot or robot teams need to create and use these models to locate critical task elements by performing appropriate sensor based actions. Here, an information-based iterative algorithm to intelligently plan the robot’s visual exploration strategy is proposed to enable it to efficiently build 3D models of its environment and task elements. The method assumes mobile robot or vehicle with cameras carried by articulated mounts. The algorithm uses the measured scene information to find the next camera position based on expected new information content of that pose. This is achieved by utilizing a metric derived from Shannon’s information theory to determine optimal sensing poses for the agent(s) mapping a highly unstructured environment. Once an appropriate environment model has been built, the quality of the information content in the model is used to determine the constraint-based optimum view for task execution. Experimental demonstrations on a cooperative robot platform performing an assembly task in the field show the effectiveness of this algorithm for single and multiple cooperating robotic systems.

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Hysteresis in Interoperability: Workflows Involving Multiple Representations of 3D Models

Volume 2B: 42nd Design Automation Conference ◽

10.1115/detc2016-59700 ◽

2016 ◽

Cited By ~ 1

Author(s):

Prakhar Jaiswal ◽

Rahul Rai ◽

Saigopal Nelaturi

Keyword(s):

Information Content ◽

Cell Biology ◽

3D Models ◽

Future Research ◽

3D Objects ◽

Solid Models ◽

Geometrical Information ◽

Pertinent Information ◽

3D Solid

Various 3D solid model representation schemes are developed to capture and process geometrical information of physical 3D objects as accurately and precisely as possible with the consideration of storage and computational complexity. These representation schemes are error prone, and their limitations prohibit them to capture all the pertinent information perfectly for a complex 3D object. Many applications in design involve repetitive conversions between several representation schemes to efficiently evaluate and operate on solid models. Mapping one representation to other degrades the quality, correctness, and completeness of the information content. In this paper, we quantify the degradation of the proxy representation models by taking inspiration from the hysteresis concept applied in different fields, such as magnetism, mechanics, control systems, cell biology, and economics. We propose a method to compute the error remanence using quantitative measures of information content and quality of proxy models. We also discuss the areas of future research such as sequencing of operations in computational work-flows that would benefit by utilizing the error remanence metric.

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