<p>    </p> <p>Image data remains an important tool for post-event building assessment and documentation. After each natural hazard event, significant efforts are made by teams of engineers to visit the affected regions and collect useful image data. In general, a global positioning system (GPS) can provide useful spatial information for localizing image data. However, it is challenging to collect such information when images are captured in places where GPS signals are weak or interrupted, such as the indoor spaces of buildings. An inability to document the images’ locations would hinder the analysis, organization, and documentation of these images as they lack sufficient spatial context. This problem becomes more urgent to solve for the inspection mission covering a large area, like a community. To address this issue, the objective of this research is to generate a tool to automatically process the image data collected during such a mission and provide the location of each image. Towards this goal, the following tasks are performed. First, I develop a methodology to localize images and link them to locations on a structural drawing (Task 1). Second, this methodology is extended to be able to process data collected from a large scale area, and perform indoor localization for images collected on each of the indoor floors of each individual building (Task 2). Third, I develop an automated technique to render the damage condition decision of buildings by fusing the image data collected within (Task 3). The methods developed through each task have been evaluated with data collected from real world buildings. This research may also lead to automated assessment of buildings over a large scale area. </p>

<p>In this dissertation, we dedicate ourselves to applying deep-learning-based computer vision algorithms to industrial applications in 2D and 3D image processing. More specifically, we present the application of deep-learning-based image processing algorithms to the following three topics: RGB-image-based shipping box defect detection, RGB-image-based patients' bed-side status monitoring, and an RGBD-image-based 3D surface video conferencing system. These projects cover 2D image detection of static objects in industrial scenarios, 2D detection of dynamic human images in bedroom environments, and accurate 3D reconstruction of dynamic humanoid objects in video conferencing. In each of these projects, we proposed ready-to-deploy pipelines combining deep learning and traditional computer vision algorithms to improve the overall performance of industrial products. In each chapter, we describe in detail how we utilize, modify, and enhance the architecture of convolutional neural networks, including the training techniques using data acquisition, image annotation, synthetic datasets, and other schemes. In the relevant sections, we also present how post-processing steps with image processing algorithms can improve the overall effectiveness of the algorithm. We hope that our work demonstrates the versatility and advantages of deep neural networks in both 2D and 3D computer vision applications.</p>

This thesis covers the topic of depth- assisted Structure from Motion (SfM). When performing classic SfM, the goal is to reconstruct a 3D scene using only a set of unstructured RGB images. What is attempted to be achieved in this thesis is adding the depth dimension to the problem formulation, and consequently create a system that can receive a set of RGBD images. The problem has been addressed by modifying an already existing SfM pipeline and in particular, its Bundle Adjustment (BA) process. Comparisons between the modified framework and the baseline framework resulted in conclusions regarding the impact of the modifications. The results show mainly two things. First of all, the accuracy of the framework is increased in most situations. The difference is the most significant when the captured scene only is covered from a small sector. However, noisy data can cause the modified pipeline to decrease in performance. Secondly, the run time of the framework is significantly reduced. A discussion of how to modify other parts of the pipeline is covered in the conclusion of the report. / Följande examensarbete behandlar ämnet djupassisterad Struktur genom Rörelse (eng. SfM). Vid klassisk SfM är målet att återskapa en 3D scen, endast med hjälp av en sekvens av oordnade RGB bilder. I djupassiterad SfM adderas djupinformationen till problemformulering och följaktligen har ett system som kan motta RGBD bilder skapats. Problemet har lösts genom att modifiera en befintlig SfM- mjukvara och mer specifikt dess Buntjustering (eng. BA). Resultatet från den modifierade mjukvaran jämförs med resultatet av originalutgåvan för att dra slutsatser rådande modifikationens påverkan på prestandan. Resultaten visar huvudsakligen två saker. Först och främst, den modifierade mjukvaran producerar resultat med högre noggrannhet i de allra flesta fall. Skillnaden är som allra störst när bilderna är tagna från endast en liten sektor som omringar scenen. Data med brus kan dock försämra systemets prestanda aningen jämfört med orginalsystemet. För det andra, så minskar exekutionstiden betydligt. Slutligen diskuteras hur mjukvaran kan vidareutvecklas för att ytterligare förbättra resultaten.

Indoor environment modeling has become a relevant topic in several applications fields including Augmented, Virtual and Mixed Reality. Furthermore, with the Digital Transformation, many industries have moved toward this technology trying to generate detailed models of an environment allowing the viewers to navigate through it or mapping surfaces to insert virtual elements in a real scene. Therefore, this Thesis project has been conducted with the purpose to review well- established deterministic methods for 3D scene reconstruction and researching the state- of- the- art, such as machine learning- based approaches, and a possible implementation on mobile devices. Initially, we focused on the well- established methods such as Structure from Motion (SfM) that use photogrammetry to estimate camera poses and depth using only RGB images. Lastly, the research has been centered on the most innovative methods that make use of machine learning to predict depth maps and camera poses from a video stream. Most of the methods reviewed are completely unsupervised and are based on a combination of two subnetwork, the disparity network (DispNet) for the depth estimation and pose network (PoseNet) for camera pose estimation. Despite the fact that the results in outdoor application show high quality depth map and and reliable odometry, there are still some limitations for the deployment of this technology in indoor environment. Overall, the results are promising. / Modellering av inomhusmiljö har blivit ett relevant ämne inom flera applikationsområden, inklusive Augmented, Virtual och Mixed Reality. Dessutom, med den digitala transformationen, har många branscher gått mot denna teknik som försöker generera detaljerade modeller av en miljö som gör det möjligt för tittarna att navigera genom den eller kartlägga ytor för att infoga virtuella element i en riktig scen. Därför har detta avhandlingsprojekt genomförts med syftet att granska väletablerade deterministiska metoder för 3Dscenrekonstruktion och undersöka det senaste inom teknik, såsom maskininlärningsbaserade metoder och en möjlig implementering på mobil. Inledningsvis fokuserade vi på de väletablerade metoderna som Structure From Motion (SfM) som använder fotogrammetri för att uppskatta kameraställningar och djup med endast RGBbilder. Slutligen har forskningen varit inriktad på de mest innovativa metoderna som använder maskininlärning för att förutsäga djupkartor och kameraposer från en videoström. De flesta av de granskade metoderna är helt utan tillsyn och baseras på en kombination av två undernätverk, skillnadsnätverket (DispNet) för djupuppskattning och posenätverk (PoseNet) för kameraposestimering. Trots att resultaten i utomhusanvändning visar djupkarta av hög kvalitet och tillförlitlig vägmätning, finns det fortfarande vissa begränsningar för användningen av denna teknik i inomhusmiljön, men ändå är resultaten lovande.

In den letzten Jahren sind immer mehr erschwingliche Kamera-Sensoren mit einer zunehmenden Vielfalt optischer Abbildungsfunktionen verfügbar geworden. Low-Cost-Optiken können aufgrund höherer Toleranzen und unterschiedlicher optischer Materialien von der gewünschten Lochkamera Metrik abweichen. Weitwinkel- und Fischaugenobjektive, verzerrende katadioptrische Objektive (spiegelnd und refraktiv) und andere ungewöhnliche Objektive weichen von der Annahme des Modells einer Lochkamera mit einer Brennweite ab. Actionkameras können die gesamte Umgebung mit zwei Objektiven abbilden, diese entsprechen meist nicht mehr dem Lochkameramodell. Kameras werden auch für Messaufgaben hinter zusätzlichen optischen Elementen eingesetzt. Die vorliegende Arbeit erweitert die ersten Erkenntnisse im Bereich der differenzierbaren (glatten) Kameramodelle ohne Einschränkungen. Viele existierende Modelle sind auf bestimmte Objektivtypen spezialisiert. In dieser Arbeit werden mehrere solcher allgemeinen Modelle eingeführt, ohne dass eine global feste Brennweite und spezielle Anforderungen an die Symmetrie der Abbildung erforderlich sind. Eine Einführung alternativer Fehlermetriken im Objektraum bringt auch enorme Rechenvorteile, da eine Abbildungsrichtung analytisch berechnet und viele der Berechnungsergebnisse konstant gehalten werden können. Zur Initialisierung solcher Modelle wird in dieser Arbeit eine generische lineare Kamera vorgestellt. Das wesentliche Merkmal dabei ist eine künstliche Transformation in höhere Dimensionen, welche mit linearen Verfahren weiterverwendet werden. Sie modellieren bereits nichtlineare Verzerrungen und Asymmetrien. Eine Multikamera-Kalibrierungssoftware wird ebenfalls beschrieben und implementiert. Das Ergebnis der Arbeit ist ein theoretischer Rahmen für glatte Kameramodelle im Objektraum selbst – anstelle der Abbildung in den Bildraum – mit mehreren konkreten Modellvorschlägen, Implementierungen und dem angepassten und erweiterten Kalibrierungsprozess. / In recent years, more and more affordable camera sensors with an increasing variety of optical imaging features have become available. Low-cost optics may deviate from the desired pinhole metric due to higher tolerances and different optical materials. Wide-angle and fisheye lenses, distorting catadioptric lenses (specular and refractive) and other unusual lenses deviate from the single focal pinhole camera model assumption, which is sometimes intentional. Action cameras can map the entire environment using two lenses, these usually no longer correspond to the pinhole camera model. Cameras are also used for measuring tasks behind additional optical elements – with unforeseeable deviations in the line of sight. The present work expands the first findings in the field of differentiable (smooth) camera models without constraints. Many existing models specialise in certain types of lenses. In this work, several such general models are introduced without requiring fixed global focal length and symmetry requirements. An introduction of alternative error metrics in the object space also gives enormous computational advantages, since one imaging direction can be calculated analytically and many of the calculation results can be kept constant. For the generation of meaningful starting values of such models, this work introduces a generic linear camera. The essential feature of is an artificial transformation into higher dimensions. These transformed coordinates can then continue to be used with linear methods. They already model non-linear distortions and asymmetries. A multi-camera calibration software that efficiently implements these models is also described and implemented. The result of the work is a theoretical framework for smooth camera models in the object space itself - instead of the established mapping into the image space - with several concrete model proposals, implementations and the adapted and extended calibration process.

Despite advances in ground-based Mobile Mapping System (MMS), automatic feature reconstruction seems far from being reached. In this thesis, we focus on 3D roadmark reconstruction from images acquired by road looking cameras of a MMS stereo-rig in dense urban context. A new approach is presented, that uses 3D geometric knowledge of roadmarks and provides a centimetric 3D accuracy with a low level of generalisation. Two classes of roadmarks are studied: zebra-crossing and dashed-lines. The general strategy consists in three main steps. The first step provides 3D linked-edges. Edges are extracted in the left and right images. Then a matching algorithm that is based on dynamic programming optimisation matches the edges between the two images. A sub-pixel matching is computed by post processing and 3D linked-edges are provided by classical photogrammetric triangulation. The second step uses the known specification of roadmarks to perform a signature based filtering of 3D linked-edges. This step provides hypothetical candidates for roadmark objects. The last step can be seen as a validation step that rejects or accepts the candidates. The validated candidates are finely reconstructed. The adopted model consists of a quasi parallelogram for each strip of zebra-crossing or dashed-line. Each strip is constrained to be flat but the roadmark as a whole is not planar. The method is evaluated on a set of 150 stereo-pairs acquired in a real urban area under normal traffic conditions. The results show the validity of the approach in terms of robustness, completeness and geometric accuracy. The method is robust and deals properly with partially occluded roadmarks as well as damaged or eroded ones. The detection rate reaches 90% and the 3D accuracy is about 2-4 cm. Finally an application of reconstructed roadmarks is presented. They are used in georeferencing of the system. Most of the MMSs use direct georeferencing devices such as GPS/INS for their localisation. However in urban areas masks and multi-path errors corrupt the measurements and provide only 50 cm accuracy. In order to improve the localisation quality, we aim at matching ground-based images with calibrated aerial images of the same area. For this purpose roadmarks are used as matching objects. The validity of this method is demonstrated on a zebra-crossing example

La scoliose idiopathique de l’adolescent est une déformation 3D du rachis. La littérature comporte une multitude d’études sur la prédiction de l’évolution et l’identification de facteurs de risque de progression. Pour l’instant les facteurs de risque établis sont l’amplitude de la déformation, la maturité squelettique et le type de courbure. Plusieurs autres champs ont été explorés comme les aspects génétiques, biochimiques, mécaniques, posturaux et topographiques, sans vraiment apporter beaucoup de précision à la prédiction de l’évolution. L’avancement de la technologie permet maintenant de générer des reconstructions 3D du rachis à l’aide des radiographies standard et d’obtenir des mesures de paramètres 3D. L’intégration de ces paramètres 3D dans un modèle prédictif représente une avenue encore inexplorée qui est tout à fait logique dans le contexte de cette déformation 3D du rachis. L’objectif général de cette thèse est de développer un modèle de prédiction de l’angle de Cobb à maturité squelettique à partir de l’information disponible au moment de la première visite, soit l’angle de Cobb initial, le type de courbure, l’âge osseux et des paramètres 3D du rachis. Dans une première étude, un indice d’âge osseux a été développé basé sur l’ossification de l’apophyse iliaque et sur le statut du cartilage triradié. Cet indice comporte 3 stades et le second stade, qui est défini par un cartilage triradié fermé avec maximum 1/3 d’ossification de l’apophyse iliaque, représente le moment pendant lequel la progression de la scoliose idiopathique de l’adolescent est la plus rapide. Une seconde étude rétrospective a permis de mettre en évidence le potentiel des paramètres 3D pour améliorer la prédiction de l’évolution. Il a été démontré qu’à la première visite il existe des différences pour 5 paramètres 3D du rachis entre un groupe de patients qui sera éventuellement opéré et un groupe qui ne progressera pas. Ces paramètres sont : la moyenne da la cunéiformisation 3D des disques apicaux, la rotation intervertébrale à la jonction inférieure de la courbure, la torsion, le ratio hauteur/largeur du corps vertébral de T6 et de la colonne complète. Les deux dernières études sont basées sur une cohorte prospective de 133 patients avec une scoliose idiopathique de l’adolescent suivi dès leur première visite à l’hôpital jusqu’à maturité squelettique. Une première étude a permis de mettre en évidence les différences morphologiques à la première visite entre les patients ayant progresser de plus ou moins de 6°. Des différences ont été mise en évidence pour la cyphose, l’angle de plan de déformation maximal, la rotation ntervertébrale l’apex, la torsion et plusieurs paramètres de «slenderness». Ensuite une seconde étude a permis de développer un modèle prédictif basé sur un modèle linéaire général en incluant l’indice d’âge osseux développé dans la première étude, le type de courbure, l’amplitude de l’angle de Cobb à la première visite, l’angle de déformation du plan maximale, la cunéiformisation 3D des disques T3-T4, T8-­T9, T11-­T12 et la somme des cunéiformisation 3D de tous les disques thoraciques et lombaires. Le coefficient de détermination multiple pour cette modélisation est de 0.715. Le modèle prédictif développé renforce l’importance de considérer la scoliose idiopathique dans les trois dimensions et il permettra d’optimiser la prédiction de l’évolution au moment de la première visite. / Prediction of curve progression remains a challenge for the clinicians at the first visit for a patient with adolescent idiopathic scoliosis. Prediction of progression is based on curve type, curve magnitude and skeletal or chronological age. The failure to accurately predict the risk of progression can lead to inadequate treatment, as well as unnecessary medical visits and radiographs. Three-­dimensional evaluation is currently more popular in the scoliosis research community either for classification or treatment planning. The global objective of this thesis was to develop a predictive model of the final Cobb angle in adolescent idiopathic scoliosis based on 3D spine parameters and on skeletal age, curve type and curve magnitude. The first study was to develop a skeletal maturity system based on ossification of iliac apophysis and triradiate cartilage status, two details available on routine radiographs. A 3 stages system was develop with the second stage being associated to the rapid acceleration phase of the deformation. The second stage corresponds to a closed triradiate cartilage and a maximum of 1/3 of the iliac apophysis ossification. The second study was a retrospective case control study. It showed the potential for 3D parameters to help predict the evolution. 3D spinal parameters at first visit of a group of patient eventually treated with surgery and a group who had no progression revealed statistical differences for: mean 3D wedging of the apical disks, lower junctional vertebral axial rotation, torsion and T6 and whole spine height/width ratio were all significantly affected. The last study was a prospective cohort study based on an adolescent idiopathic scoliosis group followed from first visit to skeletal maturity. A general linear model was developed based on 3D spinal parameters and on curve type, Cobb angle at first visit and with the 3 stages skeletal maturity system developed in the first study. The predictive model obtained has a determination coefficient of 0,715. Included 3D parameters predictors were: the angle of the plane of maximal curvature, the 3D wedging of T3-­T4, T8-­T9 and T11-­T12 disks, and the sum of 3D wedging of all thoracic and lumbar disks. The predictive model developed reinforced the importance of considering adolescent idiopathic scoliosis in a three-­‐dimensional point of view. This model will also optimise the prediction of evolution at first visit.

Les travaux réalisés dans cette thèse s'inscrivent dans le cadre du développement de techniques de mesure optiques pour la mécanique des fluides visant la reconstruction de volumes de particules 3D pour ensuite en déduire leurs déplacements. Cette technique de mesure volumique appelée encore Tomo-PIV est apparue en 2006 et a fait l'objet d'une multitude de travaux ayant pour objectif l'amélioration de la reconstruction qui représente l'une des principales étapes de cette technique de mesure. Les méthodes proposées en littérature ne prennent pas forcément en compte la forme particulière des objets à reconstruire et ne sont pas suffisamment robustes pour faire face au bruit présent dans les images. Pour pallier à ce déficit, nous avons proposé une méthode de reconstruction tomographique, appelée (IOD-PVRMPP), qui se base sur les processus ponctuels marqués. Notre méthode permet de résoudre le problème de manière parcimonieuse. Elle facilite l'introduction de l'information à priori et résout les problèmes de mémoire liés aux approches dites "basées voxels". La reconstruction d'un ensemble de particules 3D est obtenue en minimisant une fonction d'énergie ce qui définit le processus ponctuel marqué. A cet effet, nous utilisons un algorithme de recuit simulé basé sur les méthodes de Monte-Carlo par Chaines de Markov à Saut Réversible (RJMCMC). Afin d'accélérer la convergence du recuit simulé, nous avons développé une méthode d'initialisation permettant de fournir une distribution initiale de particules 3D base sur la détection des particules 2D localisées dans les images de projections. Enfin cette méthode est appliquée à des écoulements fluides soit simulé, soit issu d'une expérience dans un canal turbulent à surface libre. L'analyse des résultats et la comparaison de cette méthode avec les méthodes classiques montrent tout l'intérêt de ces approches parcimonieuses. / The research work fulfilled in this thesis fit within the development of optical measurement techniques for fluid mechanics. They are particularly related to 3D particle volume reconstruction in order to infer their movement. This volumetric measurement technic, called Tomo-PIV has appeared on 2006 and has been the subject of several works to enhance the reconstruction, which represents one of the most important steps of this measurement technique. The proposed methods in Literature don't necessarily take into account the particular form of objects to reconstruct and they are not sufficiently robust to deal with noisy images. To deal with these challenges, we propose a tomographic reconstruction method, called (IOD-PVRMPP), and based on marked point processes. Our method allows solving the problem in a parsimonious way. It facilitates the introduction of prior knowledge and solves memory problem, which is inherent to voxel-based approaches. The reconstruction of a 3D particle set is obtained by minimizing an energy function, which defines the marked point process. To this aim, we use a simulated annealing algorithm based on Reversible Jump Markov Chain Monte Carlo (RJMCMC) method. To speed up the convergence of the simulated annealing, we develop an initialization method, which provides the initial distribution of 3D particles based on the detection of 2D particles located in projection images. Finally, this method is applied to simulated fluid flow or real one produced in an open channel flow behind a turbulent grid. The results and the comparisons of this method with classical ones show the great interest of this parsimonious approach.

La tomographie par rayons X ou CT pour "Computed Tomography" est un outil puissant pour caractériser et localiser les défauts internes et pour vérifier la conformité géométrique d’un objet. Contrairement au cas des applications médicales, l’objet inspecté en Contrôle Non Destructif (CND) peut être très grand et composé de matériaux de haute atténuation, auquel cas l’utilisation d’une trajectoire circulaire pour l’inspection est impossible à cause de contraintes dans l’espace. Pour cette raison, l’utilisation de bras robotisés est l’une des nouvelles tendances reconnues dans la CT, car elle autorise plus de flexibilité dans la trajectoire d’acquisition et permet donc la reconstruction 3D de régions difficilement accessibles dont la reconstruction ne pourrait pas être assurée par des systèmes de tomographie industriels classiques. Une cellule de tomographie X robotisée a été installée au CEA. La plateforme se compose de deux bras robotiques pour positionner et déplacer la source et le détecteur en vis-à-vis. Parmi les nouveaux défis posés par la tomographie robotisée, nous nous concentrons ici plus particulièrement sur la limitation de l’ouverture angulaire imposée par la configuration en raison des contraintes importantes sur le mouvement mécanique de la plateforme. Le deuxième défi majeur est la troncation des projections qui se produit lorsque l’objet est trop grand par rapport au détecteur. L’objectif principal de ce travail consiste à adapter et à optimiser des méthodes de reconstruction CT pour des trajectoires non standard. Nous étudions à la fois des algorithmes de reconstruction analytiques et itératifs. Avant d’effectuer des inspections robotiques réelles, nous comptons sur des simulations numériques pour évaluer les performances des algorithmes de reconstruction sur des configurations d’acquisition de données. Pour ce faire, nous utilisons CIVA, qui est un outil de simulation pour le CND développé au CEA et qui est capable de simuler des données de projections réalistes correspondant à des configurations d’acquisition définies par l’utilisateur. / X-ray computed tomography (CT) is a powerful tool to characterize or localize inner flaws and to verify the geometric conformity of an object. In contrast to medical applications, the scanned object in non-destructive testing (NDT) might be very large and composed of high-attenuation materials and consequently the use of a standard circular trajectory for data acquisition would be impossible due to constraints in space. For this reason, the use of robotic arms is one of the acknowledged new trends in NDT since it allows more flexibility in acquisition trajectories and therefore could be used for 3D reconstruction of hardly accessible regions that might be a major limitation of classical CT systems. A robotic X-ray inspection platform has been installed at CEA LIST. The considered system integrates two robots that move the X-ray generator and detector. Among the new challenges brought by robotic CT, we focus in this thesis more particularly on the limited access viewpoint imposed by the setup where important constraints control the mechanical motion of the platform. The second major challenge is the truncation of projections that occur when only a field-of-view (FOV) of the object is viewed by the detector. Before performing real robotic inspections, we highly rely on CT simulations to evaluate the capability of the reconstruction algorithm corresponding to a defined scanning trajectory and data acquisition configuration. For this purpose, we use CIVA which is an advanced NDT simulation platform developed at CEA and that can provide a realistic model for radiographic acquisitions and is capable of simulating the projection data corresponding to a specific CT scene defined by the user. Thus, the main objective of this thesis is to develop analytical and iterative reconstruction algorithms adapted to nonstandard trajectories and to integrate these algorithms in CIVA software as plugins of reconstruction.

Em um primeiro momento as novas tecnologias podem impulsionar acentuadamente a interação com um elemento, o que pode levar à um aprendizado significativo, mas esse impulso reduz assim que a interação se torna comum ou até mesmo repetitiva. Quando essa nova tecnologia se torna natural para o usuário ela deixa de ser uma novidade e se torna uma ferramenta. O uso de Imagens Geradas por Computador (IGC) experienciaram exatamente isso, décadas atrás, mas estão constantemente sendo iteradas de acordo com suas necessidades de reavaliação frequentes. Com o desenvolvimento das IGC as imagens tridimensionais deixaram de ser um formato excessivamente complicado, ao passo que hardwares e conceitos foram adentrando objetos do dia-a-dia como smartphones, webcams, câmeras, aplicativos de geração de malhas 3D, etc. O seu uso com objetivos museológicos se tornou evidente no campo da herança cultural para arquivamento e comunicação. Sendo assim, para verificar a viabilidade para uma solução fácil e de baixo custo visando novos usuários, diferentes tipos de métodos não-destrutivos de reconstrução baseadas na superfície foram analisados. Para isso, identificou-se a qualidade do resultado de precisão da malha, a rastreabilidade e a compatibilidade dos mesmos. Com esse objetivo, foi proposto um método com um conjunto de métricas que podem ser aplicadas para determinar a usabilidade de um objeto reconstruído com um fim específico. Quatro artefatos arqueológicos foram escaneados usando métodos de vídeo fotogrametria e vídeo de profundidade, a serem comparados com substitutos escaneados a laser. Depois de analisar os escaneamentos dos mesmos objetos com esses diferentes métodos, concluiu-se que a fotogrametria é capaz de gerar com rapidez um modelo altamente detalhado, mas com várias distorções. A profundidade de câmera gerou superfícies mais suaves e maior incidência de erros. Em última análise, cada método apresentado demonstra múltiplas possibilidades para materialização, dependendo do objetivo, resolução e de quão detalhado o objeto deve ser para ser corretamente compreendido. / At first glance new technologies can provide an engaging way to interact with a subject which may induce a meaningful learning, but it soon falls short when it becomes common or even repetitive. As this new technology becomes natural to the user, it no longer relies on novelty and goes into another condition, as a tool. The use of Computer-Generated Imagery (CGI) experienced exactly this, decades ago, but as it\'s constantly being iterated upon it needs to be reassessed often. As CGI goes, the tridimensional imagery as an overcomplicated format started to fade, as new hardware and concepts made way into everyday objects such as smartphones, webcams, cameras, 3D mesh generation apps, etc. It\'s use for museological purposes became clear in the field of cultural heritage for archiving and communication. So, to verify the viability for a low-cost and easy to use solution aiming to novice users, different types of non-destructive methods surface based reconstructions are analyzed to identify the quality of the resulted mesh based on precision, traceability and compatibility. To this end, it was proposed a method with a set of metrics which can be used to point out the usability of a reconstructed object for a specific end. Four archaeological artifacts were scanned using the video photogrammetry method and the depth video method, to be compared with a laser scanned surrogate. After analyzing the scans of the same objects with these different methods, the conclusion is that photogrammetry has the power to provide a highly detailed model very fast but with several distortions. The depth camera provided smoother surfaces and higher error. Ultimately, each method presented multiple possibilities for materialize itself, depending on target resolution and how detailed the object must be to correctly understand it.