Selecting surface features for accurate multi-camera surface reconstruction

This paper proposes a novel feature detector for selecting local textures that are suitable for accurate multi-camera surface reconstruction, and in particular planar patch fitting techniques. This approach is in contrast to conventional feature detectors, which focus on repeatability under scale and affine transformations rather than suitability for multi-camera reconstruction techniques. The proposed detector selects local textures that are sensitive to affine transformations, which is a fundamental requirement for accurate patch fitting. The proposed detector is evaluated against the SIFT detector on a synthetic dataset and the fitted patches are compared against ground truth. The experiments show that patches originating from the proposed detector are fitted more accurately to the visible surfaces than those originating from SIFT keypoints. In addition, the detector is evaluated on a performance capture studio dataset to show the real-world application of the proposed detector

Selecting surface features for accurate multi-camera surface reconstruction

This paper proposes a novel feature detector for selecting local textures that are suitable for accurate multi-camera surface reconstruction, and in particular planar patch fitting techniques. This approach is in contrast to conventional feature detectors, which focus on repeatability under scale and affine transformations rather than suitability for multi-camera reconstruction techniques. The proposed detector selects local textures that are sensitive to affine transformations, which is a fundamental requirement for accurate patch fitting. The proposed detector is evaluated against the SIFT detector on a synthetic dataset and the fitted patches are compared against ground truth. The experiments show that patches originating from the proposed detector are fitted more accurately to the visible surfaces than those originating from SIFT keypoints. In addition, the detector is evaluated on a performance capture studio dataset to show the real-world application of the proposed detector

2D Reconstruction of Small Intestine's Interior Wall

Examining and interpreting of a large number of wireless endoscopic images from the gastrointestinal tract is a tiresome task for physicians. A practical solution is to automatically construct a two dimensional representation of the gastrointestinal tract for easy inspection. However, little has been done on wireless endoscopic image stitching, let alone systematic investigation. The proposed new wireless endoscopic image stitching method consists of two main steps to improve the accuracy and efficiency of image registration. First, the keypoints are extracted by Principle Component Analysis and Scale Invariant Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable keypoints. Second, the optimal transformation parameters obtained from first step are fed to the Normalised Mutual Information (NMI) algorithm as an initial solution. With modified Marquardt-Levenberg search strategy in a multiscale framework, the NMI can find the optimal transformation parameters in the shortest time. The proposed methodology has been tested on two different datasets - one with real wireless endoscopic images and another with images obtained from Micro-Ball (a new wireless cubic endoscopy system with six image sensors). The results have demonstrated the accuracy and robustness of the proposed methodology both visually and quantitatively.Comment: Journal draf

AUTOMATIC REGISTRATION OF MULTI-SOURCE MEDIUM RESOLUTION SATELLITE DATA

Multi-temporal and multi-source images gathered from satellite platforms are nowadays a fundamental source of information in several domains. One of the main challenges in the fusion of different data sets consists in the registration issue, i.e., the integration into the same framework of images collected with different spatial resolution and acquisition geometry. This paper presents a novel methodology to accomplish this task on the basis of a method that stands out from existing approaches. The whole data (time series) set is simultaneously co-registered with a two-dimensional multiple Least Squares adjustment with different geometric transformations implemented. Some tests were carried out with different geometric transformation models (including similarity, affine, and polynomial) and variable matching thresholds. They showed a sub-pixel precision after the computation of multiple adjustment. The use of multi-image corresponding points allowed the improvement of the registration accuracy and reliability of a time series made up of data imaged with different sensors

Uncertainty Estimation of Dense Optical Flow for Robust Visual Navigation.

This paper presents a novel dense optical-flow algorithm to solve the monocular simultaneous localisation and mapping (SLAM) problem for ground or aerial robots. Dense optical flow can effectively provide the ego-motion of the vehicle while enabling collision avoidance with the potential obstacles. Existing research has not fully utilised the uncertainty of the optical flow-at most, an isotropic Gaussian density model has been used. We estimate the full uncertainty of the optical flow and propose a new eight-point algorithm based on the statistical Mahalanobis distance. Combined with the pose-graph optimisation, the proposed method demonstrates enhanced robustness and accuracy for the public autonomous car dataset (KITTI) and aerial monocular dataset

INDOOR POSITIONING BY VISUAL-INERTIAL ODOMETRY

Indoor positioning is a fundamental requirement of many indoor location-based services and applications. In this paper, we explore the potential of low-cost and widely available visual and inertial sensors for indoor positioning. We describe the Visual-Inertial Odometry (VIO) approach and propose a measurement model for omnidirectional visual-inertial odometry (OVIO). The results of experiments in two simulated indoor environments show that the OVIO approach outperforms VIO and achieves a positioning accuracy of 1.1 % of the trajectory length

Gestión y explotación de la información TLS de 3D a 2D y 2.5D : análisis, selección y síntesis a partir de la tecnología escáner láser terrestre

The foundation and guarantee of a good heritage protection is the appropriate knowledge of it, this understanding is developed from studies conducted by different specialists, using the architectural surveys as a basis for their work and as a platform for communication between disciplines. These surveys are conducted at different levels, covering different issues, such as studying the consolidation of paintings or analysis of the structures, all in the same heritage building. This generates duplication in registration activities, as a first step in the exploration of the patrimonial goods, in most cases with difficult information to superimpose or link, which makes it difficult to cross-reading this knowledge, with records or documentation do not follow standards that allow integration, or adequate information management between surveys. Therefore the importance of developing methodologies to unify criteria between disciplines and facilitate the approximation of the different views about the heritage from a common base information, not only as an ethical obligation for those involved in conservation processes, but as tools that produce immediate benefits in management, project planning, control of current status, interdisciplinary communication and evaluation of results. To this end we work with technology Terrestrial Laser Scanner (TLS), which allows complex surveys , different leveIs of approximation, in short periods of time without compromising accuracy. The central aim of the thesis is to identify the potential of databases TLS as 20 and 2.50 images, maintaining the relationship with the initial information. Is proposed that the creation and exploitation of TLS data bases in two dimensions , can increase knowledge of heritage buildings, used for the analysis, selection classification and recognition of 3D point clouds and 3D data conversion to 2D (2.50) using image processing and synthesis process in search of a useful tool for the different disciplines that use this information starting representation. We use the concept of architectural survey (understood as analysis, selection and synthesis of real event) as framework for the development of the methodologies proposed. Considering the analysis of the asset from data collection, to the objective classification of the component parts and their relationship. After this process is selected and restructures the information, plans and elevations, to pass data from 3D to 2D (2 .5D), always maintaining the relationship with 3D starting point, achieving a synthesis that allows different levels approach to process coregistered images . In this process it was necessary to integrate techniques of paralllel lines to the TLS, formats like LIDAR aerial surveys, classification techniques of natural surfaces and reverse engineering, image processing techniques and architectural surveys SFM (Structural From Motion, processes scanned photo), therefore their developments in point cloud management and data processing 2D and 2.5D. These processes have been applied to specific case studies, highlighting mainly the geometrical characteristics of the surfaces in relation to the materials , the types of gradient, the consolidation of materials, the analysis of movements or deformations of facades , and archaeological stratification processes, among others. Parallel to the case studies, interviews validated the results, from the perspective of different disciplines were developedPodemos afirmar que la base y la garantía de una buena protección del patrimonio es el conocimiento adecuado del mismo. Este se desarrolla a partir de estudios realizados por diferentes especialistas, que utilizan como base para su trabajo y como medio de comunicación los levantamientos arquitectónicos. Estos levantamientos se realizan a diferentes escalas, abarcando problemas diferenciados, como por ejemplo el estudio de la consolidación de pinturas o el análisis de las estructuras, en un mismo edificio patrimonial. Esto conlleva a duplicar las actividades de registro, como primera etapa en la exploración de los bienes patrimoniales, en la mayoría de los casos con informaciones difíciles de superponer o relacionar, lo cual hace más difícil una lectura transversal de este conocimiento, con registros o documentaciones que no siguen estándares que permitan su integración, ni una adecuada gestión de la información. Por ello de la importancia de desarrollar metodologías que permitan unificar criterios entre disciplinas y faciliten la aproximación de las diferentes visiones sobre el patrimonio en un mismo material de partida, no solo como una obligación ética para los implicados en los procesos de conservación, sino como herramientas con beneficios inmediatos en la gestión, en la planificación de los proyectos, el control del estado actual, la comunicación interdisciplinar y la evaluación de los resultados. Para ello partimos de la tecnología de Escáner Láser Terrestre TLS, que permite levantamientos complejos, a diferentes niveles de aproximación, en breves periodos de tiempo, sin comprometer la precisión. Centrando el objetivo de la tesis en identificar el potencial de las bases de datos TLS como imágenes 2D y 2.5D, manteniendo la relación con la información de partida. Se plantea que la creación y explotación de bases de datos TLS en 2D, permite profundizar el conocimiento de los edificios patrimoniales, utilizando para el análisis y selección técnicas de clasificación 3D y conversión de datos de 3D a 2D (2.5D), utilizando el procesamiento de imágenes como proceso de síntesis, en busca de una representación útil, para las diferentes disciplinas que utilicen esta información de partida. Como estructura que encadena este proceso partimos del concepto de levantamiento arquitectónico (entendido como análisis, selección y síntesis del hecho real), para el desarrollo de las metodologías planteadas. Considerando el análisis del elemento patrimonial desde la toma de datos, hasta la clasificación objetiva de los elementos que lo componen y su relación. Posteriormente se selecciona y reestructura la información en plantas y alzados, al pasar los datos de 3D a 2D (2.5D), manteniendo en todo momento la relación con la base 3D de partida, logrando una síntesis que permite diferentes niveles de aproximación, al procesar las imágenes corregistradas, en las cuales se logra una representación patrimonial coherente. En este proceso fue necesario integrar técnicas de líneas paralelas a la TLS, como los formatos de levantamientos LIDAR aéreos, técnicas de clasificación de superficies naturales y de ingeniería inversa, técnicas de procesamiento de imágenes y de levantamientos SFM (Structural From Motion, procesos de foto escaneado), tanto por sus desarrollos en gestión de nubes de puntos o el tratamiento de datos en 2D y 2.5D. Estos procesos se aplican a casos de estudio concretos, que resaltan sobre todo las características geométricas de las superficies, en relación a los revestimientos de los materiales, las tipologías de degradado, la consolidación de los materiales, el análisis de movimientos o deformaciones de las fachadas, los procesos de estratificación arqueológica, entre otros. Paralelo a los casos de estudio, se desarrollaron entrevistas que validaron los resultados obtenidos, desde la perspectiva de diferentes disciplinas

RANSAC for Robotic Applications: A Survey

Random Sample Consensus, most commonly abbreviated as RANSAC, is a robust estimation method for the parameters of a model contaminated by a sizable percentage of outliers. In its simplest form, the process starts with a sampling of the minimum data needed to perform an estimation, followed by an evaluation of its adequacy, and further repetitions of this process until some stopping criterion is met. Multiple variants have been proposed in which this workflow is modified, typically tweaking one or several of these steps for improvements in computing time or the quality of the estimation of the parameters. RANSAC is widely applied in the field of robotics, for example, for finding geometric shapes (planes, cylinders, spheres, etc.) in cloud points or for estimating the best transformation between different camera views. In this paper, we present a review of the current state of the art of RANSAC family methods with a special interest in applications in robotics.This work has been partially funded by the Basque Government, Spain, under Research Teams Grant number IT1427-22 and under ELKARTEK LANVERSO Grant number KK-2022/00065; the Spanish Ministry of Science (MCIU), the State Research Agency (AEI), the European Regional Development Fund (FEDER), under Grant number PID2021-122402OB-C21 (MCIU/AEI/FEDER, UE); and the Spanish Ministry of Science, Innovation and Universities, under Grant FPU18/04737

Utilizando fotografias digitais de alta qualidade na geração de textura para modelos 3D

Resumo: A Preservação Digital 3D é uma área da Computação Gráfica que visa gerar modelos tridimensionais virtuais de objetos que possuem valor cultural ou cientifico. A preservação digital possibilita a visualização realística do objeto através de museus virtuais ou aplicações científicas; e a restauração do objeto preservado, em caso de desgaste natural ou acidentes. Nesta área, a representação detalhada das caracteristicas do objeto é essencial, visto que armazena informações importantes sobre o objeto preservado. Neste contexto, este trabalho apresenta um estudo sobre a geração de textura para modelos tridimensionais. Nele, é feita uma revisão sobre a modelagem da geometria e da fotometria, e é desenvolvido um algoritmo para preservar a aparência do objeto original através do uso de fotografias de alta resolução na geração de textura para o modelo 3D. Os modelos 3D renderizados com as texturas obtidas através do processo desenvolvido neste trabalho são exibidos em um museu virtual. Entre os patrim^onios digitalizados estão artefatos indígenas pertencentes ao acervo do Museu de Arqueologia e Etnologia da UFPR, e conchas e fósseis pertencentes ao Museu de Ciências Naturais da UFPR. O algoritmo desenvolvido calcula a textura de um objeto a partir do seu modelo 3D e um conjunto de imagens obtidas por um scanner a laser e uma câmera fotográfica de alta resolução. O método desenvolvido gera texturas de alta qualidade, aumentando substancialmente o realismo do modelo 3D em comparação com texturas geradas apenas por imagens do scanner. Ele também não requer nenhum aparato especial ou um grande número de fotografias coloridas, simplificando seu uso por outros pesquisadores