Quality Of 3D Model Research Articles

A Virtual Reality Tool for Accuracy Assessment of 3D Models in an Immersive Virtual Environment

Abstract. Accurate validation and assessment techniques are essential for ensuring the reliability of spatial reconstructions derived from photogrammetry, enabling well-informed decision-making across diverse domains. This study presents a Virtual Reality (VR) based accuracy assessment tool tailored for evaluating the accuracy and quality of 3D models generated by Unmanned Aerial Vehicles (UAVs). Leveraging the Unity game engine platform, our workflow entails three key steps: aligning real-world coordinates with an arbitrary Unity coordinate system, transforming the positions of Ground Control Points (GCPs) from field survey to the arbitrary system using a reference GCP, and marking observed points on the 3D models. Absolute and Relative Root Mean Square Errors (RMSE), Mean Errors (ME), and Standard Deviation of errors (SD) are computed within the virtual environment via the game object transform properties. The error distributions around each GCP are visually depicted using Unity game engine components for enhanced interaction and comprehension. The efficacy of the tool is validated through experimentation on four 3D models generated from varying camera angles during UAV data capture. The tool provides the opportunity to directly interact with the 3D models and visualize the errors, which is quite distinct from traditional methods. Using the developed tool, results were obtained to indicate that configurations employing camera angles of 60° + 75º exhibit notable performance in terms of relative and absolute accuracy.

Deep Learning-Based Workflow for Bone Segmentation and 3D Modeling in Cone-Beam CT Orthopedic Imaging

In this study, a deep learning-based workflow designed for the segmentation and 3D modeling of bones in cone beam computed tomography (CBCT) orthopedic imaging is presented. This workflow uses a convolutional neural network (CNN), specifically a U-Net architecture, to perform precise bone segmentation even in challenging anatomical regions such as limbs, joints, and extremities, where bone boundaries are less distinct and densities are highly variable. The effectiveness of the proposed workflow was evaluated by comparing the generated 3D models against those obtained through other segmentation methods, including SegNet, binary thresholding, and graph cut algorithms. The accuracy of these models was quantitatively assessed using the Jaccard index, the Dice coefficient, and the Hausdorff distance metrics. The results indicate that the U-Net-based segmentation consistently outperforms other techniques, producing more accurate and reliable 3D bone models. The user interface developed for this workflow facilitates intuitive visualization and manipulation of the 3D models, enhancing the usability and effectiveness of the segmentation process in both clinical and research settings. The findings suggest that the proposed deep learning-based workflow holds significant potential for improving the accuracy of bone segmentation and the quality of 3D models derived from CBCT scans, contributing to better diagnostic and pre-surgical planning outcomes in orthopedic practice.

Dynamically documenting archaeological excavations based on 3D modeling: a case study of the excavation of the #3 fossil of hominin cranium from Yunxian, Hubei, China

Documenting tangible cultural heritage using 3D modeling techniques is gradually becoming an indispensable component of archaeological practice. The 3D modeling techniques based on photogrammetry and LiDAR scanning enable high-accuracy and high-realistic reconstruction of archaeological sites, and have been proven a powerful tool for documenting archaeological excavations. However, dynamically documenting an ongoing excavation using these techniques is still considered tedious, time-consuming, expensive, and dependent on expertise. Moreover, the application of 3D modeling techniques in archaeological excavations still faces some technical challenges, such as modeling with multi-source and multi-scale data, fusing local models at different times into a whole, achieving fast modeling while GPU workstations are not available in the field, and evaluating the quality of 3D models. As a result, there are still very few archaeological teams deeply engaged in dynamic documentation with 3D modeling techniques, and traditional drawing sketches and taking photographs still dominate. In these senses, documenting the archaeological excavation at the Yunxian Man site (located in Hubei, China) is an invaluable opportunity for exploration and practice. Archaeologists determined to conduct dynamically documenting at the beginning of the 6th excavation project for the site, and established a rotation system to reconcile physical excavation with digital preservation. Through repeated practice and communication, we proposed a workflow and pursued several new methods to enhance the feasibility of dynamically documenting, and obtained 4D models of the ongoing archaeological excavations. In 2022, the Yunxian Man site unearthed the most intact fossil of hominin cranium from about one million years ago in the Eurasian continent, preserving important and scarce anatomical features of early humans in Asia. As the original taphonomic context of the fossil corroded away during physical excavations, the digital documentation consisting of 4D models serves as permanent original data source in subsequent archaeological research. Moreover, we obtained cross-scale 3D models from geographical environment to archaeological site, excavation area, and cultural remains, and all of these 3D models are in an actual, unified coordinate framework. Thus, we can contribute to multidisciplinary cross-collaborative research through data sharing. Considering that digital documentations serve a great value in archaeological research, this paper focuses on sharing the workflow and methods to facilitate digital preservation for more archaeological projects.

A Review of 3D Modalities Used for the Diagnosis of Scoliosis.

Spine radiographs in the standing position are the recommended standard for diagnosing idiopathic scoliosis. Though the deformity exists in 3D, its diagnosis is currently carried out with the help of 2D radiographs due to the unavailability of an efficient, low-cost 3D alternative. Computed tomography (CT) and magnetic resonance imaging (MRI) are not suitable in this case, as they are obtained in the supine position. Research on 3D modelling of scoliotic spine began with multiplanar radiographs and later moved on to biplanar radiographs and finally a single radiograph. Nonetheless, modern advances in diagnostic imaging have the potential to preserve image quality and decrease radiation exposure. They include the DIERS formetric scanner system, the EOS imaging system, and ultrasonography. This review article briefly explains the technology behind each of these methods. They are compared with the standard imaging techniques. The DIERS system and ultrasonography are radiation free but have limitations with respect to the quality of the 3D model obtained. There is a need for 3D imaging technology with less or zero radiation exposure and that can produce a quality 3D model for diseases like adolescent idiopathic scoliosis. Accurate 3D models are crucial in clinical practice for diagnosis, planning surgery, patient follow-up examinations, biomechanical applications, and computer-assisted surgery.

Performance Evaluation and Optimization of 3D Models from Low-Cost 3D Scanning Technologies for Virtual Reality and Metaverse E-Commerce

Virtual Reality (VR) is and will be a key driver in the evolution of e-commerce, providing an immersive and gamified shopping experience. However, for VR shopping spaces to become a reality, retailers’ product catalogues must first be digitised into 3D models. While this may be a simple task for retail giants, it can be a major obstacle for small retailers, whose human and financial resources are often more limited, making them less competitive. Therefore, this paper presents an analysis of low-cost scanning technologies for small business owners to digitise their products and make them available on VR shopping platforms, with the aim of helping improve the competitiveness of small businesses through VR and Artificial Intelligence (AI). The technologies to be considered are photogrammetry, LiDAR sensors and NeRF.In addition to investigating which technology provides the best visual quality of 3D models based on metrics and quantitative results, these models must also offer good performance in commercial VR headsets. In this way, we also analyse the performance of such models when running on Meta Quest 2, Quest Pro and Quest 3 headsets (Reality Labs, Reality Labs, CA, USA) to determine their feasibility and provide use cases for each type of model from a scalability point of view. Finally, our work describes a model optimisation process that reduce the polygon count and texture size of high-poly models, converting them into more performance-friendly versions without significantly compromising visual quality.

Photogrammetry: Adding Another Dimension to Virtual Gross Pathology Teaching.

Pathology is a discipline that relies on the description and interpretation of changes occurring in organs and tissues, and it is largely a "hands-on" experience, both during training and professional practice. Instigated by the need to provide a solution for online learning and teaching, a plethora of different approaches have been tested during the Covid-19 pandemic. The enforced inability to meet in person created the necessity to quickly replace the hands-on experience of practical classes, routinely considered the "gold standard" in undergraduate pathology teaching, with alternative and innovative digital solutions that could allow the students to appreciate most, if not all, features of the specimen to describe and interpret. Here we present a successful deployment of photogrammetry for the purpose of teaching gross veterinary pathology to undergraduate students. Fresh specimens obtained during routine diagnostic post-mortem activity have been photographed using Digital Single-Lens Reflex cameras and rendered into high quality 3D models, preserving almost unaltered morphology, color, and texture, when compared to the original specimen. Once processed using photogrammetry software, exported and uploaded into an online repository, 3D models become readily available via our digital learning platform (CANVAS) to all undergraduate students for self-study and consolidation, as well as to teaching staff for use during online lectures, traditional face-to-face classes, small group teaching and seminars. Preliminary data collected from students' feedback highlighted the positive reception from users, and the enriched learning experience, while prolonging indefinitely the availability of rare and perishable teaching material.

DEVELOPING ENHANCED AND ENRICHED 3D MODELS OF CULTURAL HERITAGE ASSETS AS PART OF THE EU CO-FUNDED 5DCULTURE PROJECT

Abstract. 5Dculture is a 24-month collaborative project co-funded by the European Union. It includes twelve partners from diverse sectors of cultural heritage, including archaeology, museums and fashion. It aims to enrich the offer of European 3D digital cultural heritage assets in the data space and fosters their reuse in important domains such as education, tourism and the wider cultural and creative sectors towards socially and economically sustainable outcomes. In particular, the project will deliver high-quality 3D content by identifying and engaging existing datasets from partners’ collections focusing on topics of fashion, archaeology and architecture, all of which occupy a central place in the vast cultural heritage of Europe. It will also develop several reuse scenarios, which will experiment with the aforementioned assets in their complexity (from high-quality to derivatives).The Discovery Programme has a long research interest in 3D digital documentation and has participated in collaborative projects in the past, notably 3D-ICONS. However, opportunities and challenges in the reuse of 3D archaeological content exist, including the reuse of 3D content to enhance visitors experience at archaeological sites and the ability to exploit 3D surrogates for different sectors (tourism, education, gaming, the arts, conservation and heritage management).This paper will present the research that has been undertaken in this context, through the development of an enhanced processing pipeline to improve the quality of 3D models of cultural heritage assets, resulting in their improved re-use. It will present the approaches to improving model geometry, generating and applying enhanced textures, creating rich and functional metadata and strategies for persistence and archiving.A core component of our research has been the development of the processing pipeline to improve 3D model appearance through the application of parametric materials in the development of synthetic textures which artificially enhance the visualising of cultural heritage objects. Through the visual enhancements of 3D content improvements to the interpretation and aesthetic appearance of monuments and artefacts can be achieved, adding value to the original surveyed data. The paper will detail the processing pipeline, working from prime survey data in survey specific software, through 3D modelling techniques, and finally visualising and presentation approaches which enable increased accessibility and usability.The paper will conclude with a discussion of deliverables, particularly in the context of value to conservation. The Discovery Programme works together with many of the state agencies in Ireland on heritage and conservation projects, and it is the delivery of enhanced but practical outputs that their archaeologists and architects require. These include the Office of Public Works, National Monuments Service & the Heritage Council.

Proposition of UAV multi-angle nap-of-the-object image acquisition framework based on a quality evaluation system for a 3D real scene model of a high-steep rock slope

The 3D real scene model of high-steep rock slope established based on UAV image provides convenience for non-contact identification and interpretation of rock mass structures. The quality of 3D models directly influences the interpretability of rock mass structures. However, quantitative studies on the relationship between these two aspects are scarce. Therefore, this study investigates the influence of indicators such as photography distance (D), model brightness (l), angle between photography optical axis and the discontinuity to be interpreted (θd), and texture distortion area ratio (tdp), among others, on the interpretability of the 3D models. Furthermore, it delves into the relationships among these indicators and their practical application effects in complex high-steep rock slopes. On this foundation, a novel multi-indicator quality evaluation method for 3D models constructed using UAV photogrammetry is introduced for the first time. This method, based on a composite index model and incorporating empirical approaches, classifies the quality of 3D models into five levels. Guided by the quality evaluation results and taking into account the terrain development and geometric characteristics of discontinuities, a technical framework supporting the acquisition of high-quality 3D models is proposed, known as multi-angle nap-of-the-object image acquisition (MNIA). The research indicates that the 3D models established based on MNIA exhibit significantly reduced texture distortion, leading to a substantial improvement in recognizability, with a goodness rate of 79.98%, far surpassing that of oblique photogrammetry (41.18%). This study provides crucial guidance for obtaining high-quality UAV image in complex terrain environments and holds significant engineering application value in the fine identification and interpretation of rock mass structures on high-steep rock slopes.

Systematic review of various 3D volume construction algorithms adopted in isocentric C-arm devices

C-arm systems are well established in clinical routines which produces two- dimensional (2D) projection images, which are not sufficient to provide the depth information required by surgeons during interventional procedures. Due to this, the procedural time and radiation dosage are significantly higher which leads to increased procedural cost. In order to extract depth information from 2D projection images acquired with stepwise rotation between 0 to 180 degrees, various 3D volume construction algorithms are developed. The purpose of this paper is to systematically review the various 3D volume construction algorithms in order to develop a cost-effective solution to be incorporated in the iso-centric C-arm machines without 3D volume construction algorithms. The proposed review process is done in the following aspects: i. State-of-the-art 3D volume construction algorithms ii. Comparing the different aspects of commercially available iso- centric C-arm machines incorporated with 3D volume construction algorithms. The impact of various 3D construction algorithms in improving different aspects such as image quality, resolution, procedure time, radiation dose, and accuracy of the final 3D model is discussed. Based on the findings we recommend a procedure to develop and validate a 3D volume construction algorithm. 3D volume construction from C-arm images helps to construct Multi Planar Reconstruction (MPR) views during interventional procedures which will provide in-depth information and minimize the procedure time, cost, and dosage to the patient.

Methodologies for assessing the quality of 3D models obtained using close-range photogrammetry

Although reality-based models are widely used to describe the geometric surfaces of an entity in a digital space, a systematic and universally recognised treatment of issues such as accuracy is lacking. The topic is certainly complex as this analysis should involve not only shape approximation but also other attributes (e.g., colour). Wanting to limit ourselves to geometry alone, this work proposes solutions for assessing the quality of photogrammetric models, differentiating them according to possible scenarios: sometimes, homologous models obtained using different techniques and technologies are available. In these cases, a comparison between digital reconstructions can serve to effectively quantify accuracy; more often, no terms of comparison are available, and one is forced to derive indicators from the same photogrammetric process to describe quality. We propose for this scenario a statistical analysis on the covariance matrix of the estimated coordinates for the tie points. The main goal is to provide a range of possible approaches to the conscious management of survey data.

An automated parametric ear model to improve frugal 3D scanning methods for the advanced manufacturing of high-quality prosthetic ears

Ear prostheses are commonly used for restoring aesthetics to those suffering missing or malformed external ears. Traditional fabrication of these prostheses is labour intensive and requires expert skill from a prosthetist. Advanced manufacturing including 3D scanning, modelling and 3D printing has the potential to improve this process, although more work is required before it is ready for routine clinical use. In this paper, we introduce a parametric modelling technique capable of producing high quality 3D models of the human ear from low-fidelity, frugal, patient scans; significantly reducing time, complexity and cost. Our ear model can be tuned to fit the frugal low-fidelity 3D scan through; (a) manual tuning, or (b) our automated particle filter approach. This potentially enables low-cost smartphone photogrammetry-based 3D scanning for high quality personalised 3D printed ear prosthesis. In comparison to standard photogrammetry, our parametric model improves completeness, from (81 ± 5)% to (87 ± 4)%, with only a modest reduction in accuracy, with root mean square error (RMSE) increasing from (1.0 ± 0.2) mm to (1.5 ± 0.2) mm (relative to metrology rated reference 3D scans, n = 14). Despite this reduction in the RMS accuracy, our parametric model improves the overall quality, realism, and smoothness. Our automated particle filter method differs only modestly compared to manual adjustments. Overall, our parametric ear model can significantly improve quality, smoothness and completeness of 3D models produced from 30-photograph photogrammetry. This enables frugal high-quality 3D ear models to be produced for use in the advanced manufacturing of ear prostheses.

Prediction of protein structures, functions and interactions using the IntFOLD7, MultiFOLD and ModFOLDdock servers.

The IntFOLD server based at the University of Reading has been a leading method over the past decade in providing free access to accurate prediction of protein structures and functions. In a post-AlphaFold2 world, accurate models of tertiary structures are widely available for even more protein targets, so there has been a refocus in the prediction community towards the accurate modelling of protein-ligand interactions as well as modelling quaternary structure assemblies. In this paper, we describe the latest improvements to IntFOLD, which maintains its competitive structure prediction performance by including the latest deep learning methods while also integrating accurate model quality estimates and 3D models of protein-ligand interactions. Furthermore, we also introduce our two new server methods: MultiFOLD for accurately modelling both tertiary and quaternary structures, with performancewhich has been independently verified to outperform the standard AlphaFold2 methods, and ModFOLDdock, which provides world-leading quality estimates for quaternary structure models. The IntFOLD7, MultiFOLD and ModFOLDdock servers are available at: https://www.reading.ac.uk/bioinf/.

Research on 3D reconstruction of small size objects using structure from motion photogrammetry via smartphone images

According to the recent developments on image acquisition via relatively low-cost devices such as smartphones or tablets, researches on Structure-from-Motion (SfM) photogrammetry-based 3 dimensional (3D) model reconstruction become more popular and wide frequent practice especially for study areas in geology/geomorphology, historical heritage, forestry, etc. Thus, this paper demonstrates accuracy assessment of SfM photogrammetry-based 3D model reconstruction of small size objects by altering number of the source imagery captured by smartphone and represents change detection of the generated datasets as (i) cloud-to-cloud and (ii) mesh-to-cloud comparisons. The number of images was decreased as 25% of each datasets belonging three different detail types of small size objects as the Safranbolu miniature house (SMH), a trinket made of a combination of historical buildings in Rome (HBR), and a wooden object (WO). A total of 12 datasets were generated and 9 cloud-to-cloud, and 21 mesh-to-cloud comparisons were performed. Since the obtained results show that the quality of 3D models of objects varies according to their shapes and sizes, change detection analyses show that the detail level of the objects are highly correlated with the resultant model accuracy.

Wavelength Selection Using a Modified Camera to Improve Image-Based 3D Reconstruction of Heritage Objects

ABSTRACT Tools for image-based 3D-reconstruction are commonly used for cultural heritage applications; however, wider usage has increased variability in the quality of output 3D models. Geometric variations between 3D models acquired with differing methods make metric conservation applications such as condition monitoring and measuring change over time challenging. This article presents an investigation of wavelength selection using a modified off-the-shelf DSLR camera and bandpass filters to improve input image quality in a 3D-reconstruction study of a wooden sculpture of a coyote and turtle from the Smithsonian American Art Museum. The sculpture has a large crack of concern to conservators, but its curved, dark shiny surface challenges image-based dimensional monitoring. Selecting infrared wavelengths rather than the visible light for 3D reconstruction input images reduced specular surface reflections and improved image contrast resulting in improved recording of the 3D shape. 3D-reconstructions using infrared radiation produce better reconstructions than those using visible light. In this case reconstructed surface discrepancies between visible light are ∼0.6 mm whilst those using infrared are ∼0.3 mm. Results suggest that reflected infrared images are more forgiving and flexible for recording 3D data over time for dark, shiny wooden surfaces and thus improve the reliability and comparability of image-based 3D-reconstruction.

Texture-Mapping Error Removal Based on the BRIEF Operator in Image-Based Three-Dimensional Reconstruction

In image-based three-dimensional (3D) reconstruction, texture-mapping techniques can give the model realistic textures. When the geometric surface in some regions is not reconstructed, such as for moving cars, powerlines, and telegraph poles, the textures in the corresponding image are textured to other regions, resulting in errors. To solve this problem, this letter proposes an image consistency detection method based on the Binary Robust Independent Elementary Features (BRIEF) descriptor. The method is composed of two parts. First, each triangle in the mesh and its neighboring triangles are sampled uniformly to obtain sampling points. Then, these sampled points are projected into the visible image of the triangle, and the corresponding sampled points and their RGB color values are obtained on the corresponding image. Based on the sampled points on these images, a BRIEF descriptor is calculated for each image corresponding to that triangle. In the second step, the Hamming distance between these BRIEF descriptors is calculated, outliers are removed according to the method, and noisy images are also removed. In addition, we propose adding semantic information to Markov energy optimization to reduce errors further. The two methods effectively reduced errors in texture mapping caused by objects not reconstructed, improving the texture quality of 3D models.

Contribution of 3D modelling and printing to learning in primary schools: a case study with visually impaired students from an inclusive Biology classroom

ABSTRACT Contemporary education adopts various new visualisation techniques for content to be learned. Recently, research on the application of 3D modelling and printing (3DMP) has been expanding. However, as the latest literature reviews indicate, little research is available on the contribution of 3DMP to learning of students with blindness (SWB) and students without disabilities (SWOD). This research aims to fill the gap through an exploratory case study, conducted in an inclusive Biology classroom in a primary school. Eight SWOD and four SWB (12 years old) participated in the study. The focus of the research was on pre-interventional and post-interventional students’ knowledge about cells, and students’ views on using 3DMP in Biology education. The data was analysed using Grounded Theory. The results indicate that 3DMP contributes to students’ learning by improving the ability to enumerate, describe, and visualise the cell and its parts, by remediating some of the students’ misconceptions and by increasing communication within a classroom. In terms of encountered obstacles, SWB noted that the quality of 3D models could cause new misconceptions. Adjusting the ratio, positioning and scaling of printed objects, inaccuracy in printing, and long printing process, are some of the problems which students encounter in 3DMP.

A data driven approach to generate realistic 3D tree barks

3D models of trees are ubiquitous in video games, movies, and simulators. It is of paramount importance to generate high quality 3D models to enhance the visual content, and increase the diversity of the available models. In this work, we propose a methodology to create realistic 3D models of tree barks from a consumer-grade hand-held camera. Additionally, we present a pipeline that makes use of multi-view 3D Reconstruction and Generative Adversarial Networks (GANs) to generate the 3D models of the barks. We introduce a GAN referred to as the Depth-Reinforced-SPADE to generate the surfaces of the tree barks and the bark color concurrently. This GAN gives extensive control on what is being generated on the bark: moss, lichen, scars, etc. Finally, by testing our pipeline on different Northern-European trees whose barks exhibit radically different color patterns and surfaces, we show that our pipeline can be used to generate a broad panel of tree species’ bark.

<i>In silico</i> subtractive genomics approach characterizes a hypothetical protein (MG_476) from <i>microplasma genitalium</i> G37

Background: Mycoplasma genitalium is a gram negative, parasitic pathogenic bacterium, usually transmitted sexually into human and frequently causing urethritis in men and women as well as cervicitis and pelvic inflammation in women. This is an extremely small self-replicating entity whose genome has been sequenced. This genome sequencing is advantageous in understanding pathogenesis and identifying therapeutic targets. In this study different bioinformatics tools and databases were adopted to analyze the functions of different hypothetical proteins from M. genitalium G37. Methodology: A total of 75 hypothetical proteins (HPs) were retrieved from KEGG database, while CDD-BLAST, Pfam, and InterProScan servers were used for conserved domains analysis. After that, those HPs were broadly analyzed for physicochemical properties, subcellular localization, GO annotation, and virulence factors. Results: Based on best score, hypothetical protein MG_476 was selected for homology modelling which produced a fairly good quality 3D model. The active site within MG_476 was predicted using CASTp server that helps to explore the surface features of the protein. Other approaches include the use of NetCTL, IEDB, Bcepred, and ABCpred servers to predict the location of B and T cell epitopes. Among the CD8+ T cell epitopes tested, ILQIIMFIL scored highest (0.23718) in terms of immunogenicity. Conclusion: Moreover, this analysis recommended MG_476 as a non-homologous protein and the data generated in this study may facilitate the experimental designing of novel drug and vaccines against M. genitalium.

Volumetric wall detection in unorganized indoor point clouds using continuous segments in 2D grids

The quality of 3D models of existing buildings reconstructed from point clouds is strongly related to the segmentation process used to detect structural elements. A new wall detection method in the indoor point clouds of buildings is presented in this study. The point clouds are segmented into horizontal layers, and a concept of continuous segments in a 2D grid representation is used to extract the footprints of the wall structures, and 2D blocks are projected into 3D space to obtain the wall segments in the initial 3D point cloud. The results obtained from the execution of the proposed method demonstrate that wall blocks in indoor point clouds are detected independently of their shape. Executing the proposed method on a set of 9 in-door point clouds revealed better performance in terms of result quality and execution time compared to RANSAC. The robustness of the method can be improved by adding a classification step to eliminate non-consistent blocks.

Integration of Lidar system, mobile laser scanning (MLS) and unmanned aerial vehicle system for generation of 3d building model application: A review

Nowadays, 3D modelling has become very important because real information can be extracted from the model. UAV is system that was used for developing the 3D model and has proven capable to produce good results. However, the exploration of the integration of UAV with other technology like LiDAR and MLS to develop a more accurate and detailed model is still lacking. This study reviews the aptitudes of the UAV integrated with LiDAR and MLS for developing the 3D building model in Malaysia. Several issues, like type of platform, quality of 3D model and others to review the capabilities of the UAV used for producing very accurate 3D model were critically analysed. Previous research suggests that integration of Lidar with multi-rotor platform fusing with MLS could be used to construct an accurate 3D model. A dense point cloud of the whole building can be obtained from the fusion between Lidar and MLS. Moreover, an accurate 3D model can be generated from this process as the point-cloud from LiDAR and MLS has positional accuracy in centimetre level compared to a non-metric camera integrated with a UAV. This study finds the UAV has the potential to use to produce a quality 3D model.