Quality Of 3D Model Research Articles

A Texture Selection Approach for Cultural Artifact 3D Reconstruction Considering Both Geometry and Radiation Quality

3D reconstruction of culture artifacts has great potential in digital heritage documentation and protection. Choosing the proper images for texture mapping from multi-view images is a major challenge for high precision and high quality 3D reconstruction of culture artifacts. In this study, a texture selection approach, considering both the geometry and radiation quality for 3D reconstruction of cultural artifacts while using multi-view dense matching is proposed. First, a Markov random field (MRF) method is presented to select images from the best angle of view among texture image sets. Then, an image radiation quality evaluation model is proposed in the virtue of a multiscale Tenengrad definition and brightness detection to eliminate fuzzy and overexposed textures. Finally, the selected textures are mapped to the 3D model under the mapping parameters of the multi-view dense matching and a semi-automatic texture mapping is executed on the 3DMax MudBox platform. Experimental results with two typical cultural artifacts data sets (bronze wares and porcelain) show that the proposed method can reduce abnormal exposure or fuzzy images to yield high quality 3D model of cultural artifacts.

Applicability Assessments of Close-Range Photogrammetry for Slope Face 3D Modelling

The digital single lens reflex (DSLR) cameras have been widely accepted to use in slope face photogrammetry rather than the expensive metric camera used for aerial photogrammetry. 3D models generated from digital photogrammetry can approach those generated from terrestrial laser scanning in term of scale and level of detail. It is cost effective and has equipment portability. This paper presents and discusses the applicability of close-range digital photogrammetry to produce 3D models of rock slope faces. Five experiments of image capturing method were conducted to capture the photographs as the input data for processing. As a consideration, the appropriate baseline lengths to capture the slope face to get better result are around 1/6 to 1/8 of target distance. A fine quality of 3D model from data processing is obtained using strip method and convergent method with 80% overlapping in each photograph. A random camera positions with different distances from the slope face can also generate a good 3D model, however the entire target should be captured in each photograph. The accuracy of the models is generated by comparing the 3D models produced from photogrammetry with the 3D data obtained from laser scanner. The accuracy of 3D models is quite satisfactory with the mean error range from 0.008 to 0.018 m.

Monitoring Slope Condition Using UAV Technology

Slope failure is a serious geologic hazard in many countries in the world including Malaysia. In order to prevent slope failure, the hazardous symptoms can be detected early in slope monitoring process. Nowadays, slope failure symptoms monitoring has been done by human by on site observation at the slope spot and it is dangerous for the human safety. Furthermore, it takes longer time to complete the investigation and some of the data collected are inaccurate because human view is limited. Therefore, this project is to evaluate the performances of Unmanned Aerial Vehicle (UAV) to monitor the slope condition. A slope at Taman Harmoni Vista at Pagoh, Johor has been selected as a study area. The measurement of slope studied was 150 meters in length and 20 meters in height respectively. The aim of this project is to evaluate the performances of Unmanned Aerial Vehicle (UAV) to monitor the risk slope. Visual image of 3D modelling was obtained from Pix4D Mapper and Global Mapper software was used to analyze the slope failure symptoms. There are defects that have been identified from 3D modelling of the slope which is tension crack on slope and surface crack on the drainage. The use of UAV is an alternative method to obtain quality picture and 3D model for inspection of the slope. This method expected to facilitate the work of inspection slopes in addition to saving time, energy and cost as well asreducing the risk of harm.

FROM REALITY TO PARAMETRIC MODELS OF CULTURAL HERITAGE ASSETS FOR HBIM

Abstract. The ability of managing big amounts of metric information coming from a LiDAR survey and the ability to reproduce high quality 3D models from them are still vivid problems to solve. Is it possible to create detailed models, geometrically and metrically correct, without using a large amount (often redundant) of metric data, such as massive point clouds? Obviously yes, but there are several ways to create a fitting 3D model for a specific research. A good solution is given by NURBS based algorithms that ensure high details of modelling. However, NURBS models can't be used directly on BIM platforms, because they need to be parametrized. In this sense, a parametric model is based on real measurements but each object could be interpreted and approximated based on objective and subjective (critic) view and also based on LODs (levels of detail or development) concerning a particular analysis. This kind of modelling of Cultural Heritage assets, fundamental for HBIM creation, need to be correctly planned especially for classification and definition of its historical features connected to an informative system, because nowadays information and then the semantic dimension are a necessary key points towards documentation analysis. Established this brief introduction, this schematic work will focus on the analysis of FreeCAD open BIM software and Rhinoceros as NURBS 3D modeller for Cultural Heritage is concerned, and whether and how they could integrate their tools for the purpose of managing dynamic high detailed data for the creation of an HBIM platform.

An Application of Manifold Learning in Global Shape Descriptors

With the rapid expansion of applied 3D computational vision, shape descriptors have become increasingly important for a wide variety of applications and objects from molecules to planets. Appropriate shape descriptors are critical for accurate (and efficient) shape retrieval and 3D model classification. Several spectral-based shape descriptors have been introduced by solving various physical equations over a 3D surface model. In this paper, for the first time, we incorporate a specific manifold learning technique, introduced in statistics and machine learning, to develop a global, spectral-based shape descriptor in the computer graphics domain. The proposed descriptor utilizes the Laplacian Eigenmap technique in which the Laplacian eigenvalue problem is discretized using an exponential weighting scheme. As a result, our descriptor eliminates the limitations tied to the existing spectral descriptors, namely dependency on triangular mesh representation and high intra-class quality of 3D models. We also present a straightforward normalization method to obtain a scale-invariant and noise-resistant descriptor. The extensive experiments performed in this study using two standard 3D shape benchmarks—high-resolution TOSCA and McGill datasets—demonstrate that the present contribution provides a highly discriminative and robust shape descriptor under the presence of a high level of noise, random scale variations, and low sampling rate, in addition to the known isometric-invariance property of the Laplace–Beltrami operator. The proposed method significantly outperforms state-of-the-art spectral descriptors in shape retrieval and classification. The proposed descriptor is limited to closed manifolds due to its inherited inability to accurately handle manifolds with boundaries.

QMEANDisCo – Distance constraints applied on model quality estimation

Motivation Methods that estimate the quality of a 3D protein structure model in absence of an experimental reference structure are crucial to determine a model's utility and potential applications. Single model methods assess individual models whereas consensus methods require an ensemble of models as input. In this work, we extend the single model composite score QMEAN that employs statistical potentials of mean force and agreement terms by introducing a consensus-based distance constraint (DisCo) score. Results DisCo exploits distance distributions from experimentally determined protein structures that are homologous to the model being assessed. Feed-forward neural networks are trained to adaptively weigh contributions by the multi-template DisCo score and classical single model QMEAN parameters. The result is the composite score QMEANDisCo, which combines the accuracy of consensus methods with the broad applicability of single model approaches. We also demonstrate that, despite being the de-facto standard for structure prediction benchmarking, CASP models are not the ideal data source to train predictive methods for model quality estimation. For performance assessment, QMEANDisCo is continuously benchmarked within the CAMEO project and participated in CASP13. For both, it ranks among the top performers and excels with low response times. Availability and implementation QMEANDisCo is available as web-server at https://swissmodel.expasy.org/qmean. The source code can be downloaded from https://git.scicore.unibas.ch/schwede/QMEAN. Supplementary information Supplementary data are available at Bioinformatics online.

A submap joining algorithm for 3D reconstruction using an RGB-D camera based on point and plane features

In standard point-based methods, the depth measurements of the point features suffer from noise, which will lead to incorrect global structure of the environment. This paper presents a submap joining based SLAM with an RGB-D camera by introducing planes as well as points as features.This work is consisted of two steps: submap building and submap joining. Several adjacent keyframes, with the corresponding small patches, visual feature points, and planes observed from these keyframes, are used to build a submap. We fuse the submaps into a global map in a sequential fashion, such that, the global structure is recovered gradually through plane feature associations and optimization. We also show that the proposed algorithm can handle plane association problem incrementally in submap level, as the plane covariance can be obtained in each submap. The use of submap significantly reduces the computational cost during the optimization process, while keeping all information about planes. The proposed method is validated using both publicly available RGB-D benchmarks and datasets collected by authors. The algorithm can produce accurate trajectories and high quality 3D models on these challenging datasets, which are difficult for existing RGB-D SLAM or SFM algorithms.

A Review on the Recent Advancement in “Tumour Spheroids-on-a-Chip”

Introduction: Three-dimensional (3D) cell cultures are identified as more accurate and representative models of human cancers than conventional two-dimensional monolayer cell cultures. However, currently established 3D culturing techniques are technically challenging, time- and resource-consuming, and performed using traditional laboratory tissue culturing equipment. In recent years, microfluidics has been introduced into biomedical research, allowing cells and tissues to be cultured in microfabricated devices. The current challenge is to adapt existing 3D cell culturing techniques to microfluidic devices, allowing for the fabrication of low-cost, rapid evaluation devices to facilitate biomedical research and clinical application. The aim of this review was to evaluate microfluidics and 3D cell culture research with particular relevance to oncological research. Methods: Journal articles were acquired from different scientific databases and were identified using specific keywords. Three-Dimensional Cell Culturing Microfluidic Concepts: Various 3D cell culturing microfluidic devices have been designed, based on existing 3D cell culturing methods. This includes non-cell adherent-based devices, matrix-embedding, hanging drop, and droplet-based culturing methods. These platforms facilitate the culturing, treatment, and analysis of 3D spheroids, while simultaneously scaling down traditional experimental requirements. Limitations and Future Perspectives: Beyond superficial analysis, a major drawback in the current scope of 3D cell culturing microfluidic devices is the inability to extract spheroids for examining histology. Polydimethylsiloxane is the preferred material to fabricate devices but may need revision for commercializing microfluidic platforms in the future. Integrating 3D bioprinting and organoid cultures could potentially improve the quality of 3D models in microfluidic devices. Conclusion: 3D spheroids are an effective representation of in vivo cancers and microfluidics has streamlined the culture, treatment, and analysis of 3D models. Considerable improvements have been made in combining the two entities, but further work is required to manufacture 3D cell culturing microfluidic devices on a commercial scale.

A 3D model retrieval method based on multi-feature fusion

3D model retrieval is a hot topic in information retrieval, and it is of great importance to fuse multi-feature of 3D models to achieve high quality retrieval. Therefore, in this paper, we propose a novel 3D model retrieval method based on the multi-feature fusion technology. Motivation for the proposed 3D model retrieval method lies in that we convert the 3D model retrieval problem to a discriminative feature space mapping problem. The framework of the multi-feature fusion based 3D model retrieval system contains two main modules: 1) model normalisation; 2) multi-feature fusion. The proposed 3D model retrieval method is designed based on multiple feature fusion and online projection learning. In order to effectively fuse multiple features, we train a model to learn a low dimensional and discriminative feature space from the multiple views of 3D models. Particularly, to effectively retrieve the newly added samples, we propose an online projection learning algorithm, which learns a projection matrix by handling the least square regression model. Experimental results show that the proposed method can achieve higher precision for a given recall than others methods, that is, the proposed method can obtain higher quality 3D model retrieval results than state-of-the-art methods.

Regulatory Concerns about Medical Device Manufacturing Using 3D Printing: Current State of the Issue

Custom-made implants, orthotics, orthoses, models for surgical planning and education, and much more are now created using 3D printers. In this article, the authors suiiunarized information on laws and regulations in the domain of legal support for 3D printing of medical devices in Russia and abroad. 3D printing is one of the promising avenues in developing new methods of treatment, so iiiunediate establishing of clear criteria for its legal regulation is necessary. As is, there are still many gaps in the legislative framework. The issues of the quality of 3D models, material standardization and manufacturing processes using 3D printing technologies remain unresolved. When using custom-made medical devices, respecting the rights of patients and preventing the use of prohibited or restricted materials are essential. Yet, legal barriers to this irmovative direction of medicine must be avoided.

AutoSweep: Recovering 3D Editable Objects from a Single Photograph.

This paper presents a fully automatic framework for extracting editable 3D objects directly from a single photograph. Unlike previous methods which recover either depth maps, point clouds, or mesh surfaces, we aim to recover 3D objects with semantic parts and can be directly edited. We base our work on the assumption that most human-made objects are constituted by parts and these parts can be well represented by generalized primitives. Our work makes an attempt towards recovering two types of primitive-shaped objects, namely, generalized cuboids and generalized cylinders. To this end, we build a novel instance-aware segmentation network for accurate part separation. Our GeoNet outputs a set of smooth part-level masks labeled as profiles and bodies. Then in a key stage, we simultaneously identify profile-body relations and recover 3D parts by sweeping the recognized profile along their body contour and jointly optimize the geometry to align with the recovered masks. Qualitative and quantitative experiments show that our algorithm can recover high quality 3D models and outperforms existing methods in both instance segmentation and 3D reconstruction.

Use of a 3D model to improve the performance of laser-based railway track inspection

In recent decades, 3D reconstruction techniques have been applied in an increasing number of areas such as virtual reality, robot navigation, medical imaging and architectural restoration of cultural relics. Most of the inspection techniques used in railway systems are, however, still implemented on a 2D basis. This is particularly true of track inspection due to its linear nature. Benefiting from the development of sensor technology and constantly improving processors, higher quality 3D model reconstructions are becoming possible which push the technology into more challenging areas. One such advancement is the use of 3D perceptual techniques in railway systems. This paper presents a novel 3D perceptual system, based on a low-cost 2D laser sensor, which has been developed for the detection and characterisation of physical surface defects in railway tracks. An innovative prototype system has been developed to capture and correlate the laser scan data; dedicated 3D data processing procedures have then been developed in the form of three specific defect-detection algorithms (depth gradient, face normal and face-normal gradient) which are applied to the 3D model. The system has been tested with rail samples in the laboratory and at the Long Marston Railway Test Track. The 3D models developed represent the external surface of the samples both laterally (2D slices) and longitudinally (3D model), and common surface defects can be detected and represented in 3D. The results demonstrate the feasibility of applying 3D reconstruction-based inspection techniques to railway systems.

Study the precision of creating 3D structure modeling form terrestrial laser scanner observations

Abstract Laser scanner has become widely used nowadays for several applications in civil engineering. An advantage of laser scanner as compared to other geodetic instruments is its capability of collecting hundreds or even thousands of point per second. Terrestrial laser scanner allows acquiring easy and fast complex geometric data from building, machines, objects, etc. Several experimental and field tests are required to investigate the quality and accuracy of scanner points cloud and the 3D geometric models derived from laser scanner. So this paper investigates the precision of creation three dimensional structural model resulted from terrestrial laser scanner observations. The paper also presented the ability to create 3D model by structural faces depending on the plane equation for each face resulted from coordinates of several observed points cover this face using reflector less total station observations. Precision comparison for the quality of 3D models created from laser scanner observations and structure faces is also presented.The results of the practical measurements, calculations and analysis of results are presented.

Experimental study to prevent the warping of 3D models in fused deposition modeling

In order to perfect the process of modeling using fused deposition modeling, the problem of warping of 3D models made of materials such as ABS and PLA should be addressed properly. In this paper various methods of overcoming this defect have been discussed with some practical experimentation by the author. The solution to the problem of warping has broadly been divided by the author into three categories. One is treating the bed with chemical solutions; other is providing an enclosure and the last one is by modifying the internal structure of model. The effectiveness of treatment of hot bed with ABS–acetone solution and PVA based solution on the quality of 3D model is studied through experimentation. The effect of redesigning the base layers of 3D models to overcome warping problem is also discussed in detail through experimentation.

A Regularized Volumetric Fusion Framework for Large-Scale 3D Reconstruction

Modern computational resources combined with low-cost depth sensing systems have enabled mobile robots to reconstruct 3D models of surrounding environments in real-time. Unfortunately, low-cost depth sensors are prone to produce undesirable estimation noise in depth measurements which result in either depth outliers or introduce surface deformations in the reconstructed model. Conventional 3D fusion frameworks integrate multiple error-prone depth measurements over time to reduce noise effects, therefore additional constraints such as steady sensor movement and high frame-rates are required for high quality 3D models. In this paper we propose a generic 3D fusion framework with controlled regularization parameter which inherently reduces noise at the time of data fusion. This allows the proposed framework to generate high quality 3D models without enforcing additional constraints. Evaluation of the reconstructed 3D models shows that the proposed framework outperforms state of art techniques in terms of both absolute reconstruction error and processing time.

Prediction of disulfide dihedral angles using chemical shifts.

Cystine residues result from the formation of disulfide bonds between pairs of cysteine residues. This cross linking of the backbone is essential for the structure and activity of peptides and proteins. The conformation of a cystine side chain can be described using five dihedral angles, χ1, χ2, χ3, χ2', and χ1', with cystines favouring certain combinations of these angles. 2D NMR spectroscopy is ideally suited for structure determination of disulfide-rich peptides, because of their small size and constrained nature. However, only limited information of the cystine side chain conformation can be determined by NMR spectroscopy, leading to ambiguity in the deduced 3D structures. Resolving accurate structures is important as disulfide-rich peptides have proven to be promising drug candidates in a number of fields, either as bioactive leads or scaffolds. Using a database of NMR chemical shifts combined with crystallographic structures, we have developed a method called DISH that uses support vector machines to predict the dihedral angles of cysteine side chains. It is able to successfully predict χ2 angles with 91% accuracy, and has improved performance over existing prediction methods for χ1 angles, with 87% accuracy. For 81% of cysteine residues, DISH successfully predicted both the χ1 and χ2 angles. By revisiting published solution structures of peptides determined using NMR spectroscopy, we assessed the impact of additional cystine dihedral restraints on the quality of 3D models. DISH improved the resolution and accuracy, highlighting the potential for improving the understanding of structure-activity relationships and rational development of peptide drugs.

Testing the value of low-cost Structure-from-Motion (SfM) photogrammetry for metric and visual analysis of rock art

The discussion of digital photogrammetry and/or 3D laser scanning for recording the geometry of rock art sites is not new, but the production of high-quality 3D models requires great expenditure and specialized personnel that are not accessible to everyone or applicable to all sites. While the use of low-cost cameras and photogrammetry has been suggested as an alternative for fast visualization of rock art, do the results have enough quality for the metric and visual analysis of the art? This paper explores the potential of low-cost Structure-from-Motion (SfM) photogrammetry performed by non-professional surveyors, with no camera calibration and no tripod to speed the process, as an alternative cost-effective tool for quantitative and qualitative analysis of rock art. The innovative aspect of this paper is that we have verified the metric accuracy and graphic quality of low-cost 3D models and shown that they can be used as an effective low-cost tool for detailed analysis and tracing of rock art.

Globally Consistent Indoor Mapping via a Decoupling Rotation and Translation Algorithm Applied to RGB-D Camera Output

This paper presents a novel RGB-D 3D reconstruction algorithm for the indoor environment. The method can produce globally-consistent 3D maps for potential GIS applications. As the consumer RGB-D camera provides a noisy depth image, the proposed algorithm decouples the rotation and translation for a more robust camera pose estimation, which makes full use of the information, but also prevents inaccuracies caused by noisy depth measurements. The uncertainty in the image depth is not only related to the camera device, but also the environment; hence, a novel uncertainty model for depth measurements was developed using Gaussian mixture applied to multi-windows. The plane features in the indoor environment contain valuable information about the global structure, which can guide the convergence of camera pose solutions, and plane and feature point constraints are incorporated in the proposed optimization framework. The proposed method was validated using publicly-available RGB-D benchmarks and obtained good quality trajectory and 3D models, which are difficult for traditional 3D reconstruction algorithms.

Methods for estimation of model accuracy in CASP12.

Methods to reliably estimate the quality of 3D models of proteins are essential drivers for the wide adoption and serious acceptance of protein structure predictions by life scientists. In this article, the most successful groups in CASP12 describe their latest methods for estimates of model accuracy (EMA). We show that pure single model accuracy estimation methods have shown clear progress since CASP11; the 3 top methods (MESHI, ProQ3, SVMQA) all perform better than the top method of CASP11 (ProQ2). Although the pure single model accuracy estimation methods outperform quasi-single (ModFOLD6 variations) and consensus methods (Pcons, ModFOLDclust2, Pcomb-domain, and Wallner) in model selection, they are still not as good as those methods in absolute model quality estimation and predictions of local quality. Finally, we show that when using contact-based model quality measures (CAD, lDDT) the single model quality methods perform relatively better.

3D modelling and measurements of historical violins

Measuring historical violins provides crucial information about the morphology of the instruments, useful both for researchers and violin makers. Generally, these measures are taken manually using a calliper, but they can be repeated only occasionally due to both the restricted access to these precious instruments and the need of avoiding accidental damages to the wood or to the varnishes. In this work, we describe and assess the accuracy of a protocol for the acquisition and creation of high quality 3D models of violins, suitable for taking accurate measurements. Six historical violins of 17th – 18th centuries, kept in “Museo del Violino” in Cremona (Italy), were used as test set. The quality of the final outcomes was checked comparing measures taken on the 3D meshes with the correspondent ones taken by calliper on the original instruments. Finally, a comparison between the sound board of the instruments were performed.