50 research outputs found

    Multilevel Skeletonization Using Local Separators

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    Animals in Religion, Economy and Daily Life of Ancient Egypt and beyond

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    [Italiano]: L’International Symposium on Animals in Ancient Egypt, the Middle Nile and their hinterlands (ISAAE) è nato con l’obiettivo di riprendere il proficuo dialogo tra studiosi impegnati in questo vastissimo ambito di ricerca, instauratosi già nel 2016 in occasione della prima edizione organizzata e svoltasi al Musée de Confluences di Lione, e consolidatosi con la seconda edizione tenutasi al Cairo presso l'American University nel 2019. Il terzo Simposio è stato organizzato dal Dipartimento Asia, Africa e Mediterraneo (DAAM) dell'Università di Napoli “L'Orientale” (UniOr), in collaborazione con l'American University del Cairo, e si è svolto presso l’UniOr dal 15 al 17 giugno 2022. I tre intensi giorni di incontri e discussioni hanno rappresentato un importante e proficuo momento di condivisione e di aggiornamento sia sui temi della ricerca teorica e sul campo, sia su quelli più tecnici, connessi alle moderne tecnologie di indagine. Studiosi provenienti da tutto il mondo (Europa, Stati Uniti, Egitto, Giappone, Australia) hanno affrontato una pletora di argomenti legati agli animali: archeozoologia, macellazione, mummificazione e relative tecniche moderne di conservazione-restauro, pratiche funerarie, religione, terminologia e scrittura, arti e mestieri, alimentazione, ruolo nella vita quotidiana e nell'economia. Il presente volume ne raccoglie i risultati che gli editori sono lieti di condividere con i colleghi, ma anche con un pubblico di appassionati, a poco più di un anno dal Simposio./[English]: The International Symposium on Animals in Ancient Egypt, the Middle Nile and their hinterland (ISAAE) was established with the aim of resuming the fruitful dialogue between scholars working in this vast field of research. This initiative was first launched in 2016 during the inaugural edition held at the Musée de Confluences in Lyon and further consolidated at the second edition hosted by the American University in Cairo in 2019. The Third Symposium, organised by the Department Asia, Africa e Mediterrano (DAAM) of the University of Naples "L'Orientale" (UniOr), in collaboration with the American University in Cairo, was hosted at UniOr from 15 to 17 June 2022. The three intensive days of meetings and discussions provided a valuable opportunity to exchange and update theoretical and field research topics, as well as technical issues related to modern research technologies. Scholars from all over the world (Europe, United States, Egypt, Japan, Australia) have addressed a plethora of animal-related topics: archaeozoology, slaughter, mummification and related modern preservation-restoration techniques, funerary practices, religion, terminology and writing, arts and crafts, nutrition, role in daily life and the economy. The present volume contains the results that editors are pleased to share with colleagues as well as enthusiasts, just over a year after the symposium

    DiffComplete: Diffusion-based Generative 3D Shape Completion

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    We introduce a new diffusion-based approach for shape completion on 3D range scans. Compared with prior deterministic and probabilistic methods, we strike a balance between realism, multi-modality, and high fidelity. We propose DiffComplete by casting shape completion as a generative task conditioned on the incomplete shape. Our key designs are two-fold. First, we devise a hierarchical feature aggregation mechanism to inject conditional features in a spatially-consistent manner. So, we can capture both local details and broader contexts of the conditional inputs to control the shape completion. Second, we propose an occupancy-aware fusion strategy in our model to enable the completion of multiple partial shapes and introduce higher flexibility on the input conditions. DiffComplete sets a new SOTA performance (e.g., 40% decrease on l_1 error) on two large-scale 3D shape completion benchmarks. Our completed shapes not only have a realistic outlook compared with the deterministic methods but also exhibit high similarity to the ground truths compared with the probabilistic alternatives. Further, DiffComplete has strong generalizability on objects of entirely unseen classes for both synthetic and real data, eliminating the need for model re-training in various applications.Comment: Project Page: https://ruihangchu.com/diffcomplete.htm

    LIPIcs, Volume 258, SoCG 2023, Complete Volume

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    LIPIcs, Volume 258, SoCG 2023, Complete Volum

    Women in Artificial intelligence (AI)

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    This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI

    Normal Transformer: Extracting Surface Geometry from LiDAR Points Enhanced by Visual Semantics

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    High-quality estimation of surface normal can help reduce ambiguity in many geometry understanding problems, such as collision avoidance and occlusion inference. This paper presents a technique for estimating the normal from 3D point clouds and 2D colour images. We have developed a transformer neural network that learns to utilise the hybrid information of visual semantic and 3D geometric data, as well as effective learning strategies. Compared to existing methods, the information fusion of the proposed method is more effective, which is supported by experiments. We have also built a simulation environment of outdoor traffic scenes in a 3D rendering engine to obtain annotated data to train the normal estimator. The model trained on synthetic data is tested on the real scenes in the KITTI dataset. And subsequent tasks built upon the estimated normal directions in the KITTI dataset show that the proposed estimator has advantage over existing methods

    Symmetry-based Method for Water Level Prediction using Sentinel 2 Data

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    The Sentinel satellite constellation series, developed and operated by the European Space Agency, represents a dedicated space component of the European Copernicus Programme, committed to long-term operational services in the environment, climate and security. A huge amount of acquired data allow us different surveys. The paper considers the detection of changes in water levels in Lake Cerknica. The multispectral index has been calculated from Sentinel-2 data and transformed to a 3D point cloud. As shown by the results, symmetry measures of 3D point clouds could be used for the detection of water levels. Prediction functions using a genetic algorithm have been fitted, and the best result achieved was RMSE = 0.9824

    Point Normal Orientation and Surface Reconstruction by Incorporating Isovalue Constraints to Poisson Equation

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    Oriented normals are common pre-requisites for many geometric algorithms based on point clouds, such as Poisson surface reconstruction. However, it is not trivial to obtain a consistent orientation. In this work, we bridge orientation and reconstruction in implicit space and propose a novel approach to orient point clouds by incorporating isovalue constraints to the Poisson equation. Feeding a well-oriented point cloud into a reconstruction approach, the indicator function values of the sample points should be close to the isovalue. Based on this observation and the Poisson equation, we propose an optimization formulation that combines isovalue constraints with local consistency requirements for normals. We optimize normals and implicit functions simultaneously and solve for a globally consistent orientation. Owing to the sparsity of the linear system, an average laptop can be used to run our method within reasonable time. Experiments show that our method can achieve high performance in non-uniform and noisy data and manage varying sampling densities, artifacts, multiple connected components, and nested surfaces

    Digital Multispectral Map Reconstruction Using Aerial Imagery

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    Advances made in the computer vision field allowed for the establishment of faster and more accurate photogrammetry techniques. Structure from Motion(SfM) is a photogrammetric technique focused on the digital spatial reconstruction of objects based on a sequence of images. The benefit of Unmanned Aerial Vehicle (UAV) platforms allowed the ability to acquire high fidelity imagery intended for environmental mapping. This way, UAV platforms became a heavily adopted method of survey. The combination of SfM and the recent improvements of Unmanned Aerial Vehicle (UAV) platforms granted greater flexibility and applicability, opening a new path for a new remote sensing technique aimed to replace more traditional and laborious approaches often associated with high monetary costs. The continued development of digital reconstruction software and advances in the field of computer processing allowed for a more affordable and higher resolution solution when compared to the traditional methods. The present work proposed a digital reconstruction algorithm based on images taken by a UAV platform inspired by the work made available by the open-source project OpenDroneMap. The aerial images are inserted in the computer vision program and several operations are applied to them, including detection and matching of features, point cloud reconstruction, meshing, and texturing, which results in a final product that represents the surveyed site. Additionally, from the study, it was concluded that an implementation which addresses the processing of thermal images was not integrated in the works of OpenDroneMap. By this point, their work was altered to allow for the reconstruction of thermal maps without sacrificing the resolution of the final model. Standard methods to process thermal images required a larger image footprint (or area of ground capture in a frame), the reason for this is that these types of images lack the presence of invariable features and by increasing the image’s footprint, the number of features present in each frame also rises. However, this method of image capture results in a lower resolution of the final product. The algorithm was developed using open-source libraries. In order to validate the obtained results, this model was compared to data obtained from commercial products, like Pix4D. Furthermore, due to circumstances brought about by the current pandemic, it was not possible to conduct a field study for the comparison and assessment of our results, as such the validation of the models was performed by verifying if the geographic location of the model was performed correctly and by visually assessing the generated maps.Avanços no campo da visão computacional permitiu o desenvolvimento de algoritmos mais eficientes de fotogrametria. Structure from Motion (SfM) é uma técnica de fotogrametria que tem como objetivo a reconstrução digital de objectos no espaço derivados de uma sequência de imagens. A característica importante que os Veículos Aérios não-tripulados (UAV) conseguem fornecer, a nível de mapeamento, é a sua capacidade de obter um conjunto de imagens de alta resolução. Devido a isto, UAV tornaram-se num dos métodos adotados no estudo de topografia. A combinação entre SfM e recentes avanços nos UAV permitiram uma melhor flexibilidade e aplicabilidade, permitindo deste modo desenvolver um novo método de Remote Sensing. Este método pretende substituir técnicas tradicionais, as quais estão associadas a mão-de-obra intensiva e a custos monetários elevados. Avanços contínuos feitos em softwares de reconstrução digital e no poder de processamento resultou em modelos de maior resolução e menos dispendiosos comparando a métodos tradicionais. O presente estudo propõe um algoritmo de reconstrução digital baseado em imagens obtidas através de UAV inspiradas no estudo disponibilizado pela OpenDroneMap. Estas imagens são inseridas no programa de visão computacional, onde várias operações são realizadas, incluindo: deteção e correspondência de caracteristicas, geração da point cloud, meshing e texturação dos quais resulta o produto final que representa o local em estudo. De forma complementar, concluiu-se que o trabalho da OpenDroneMap não incluia um processo de tratamento de imagens térmicas. Desta forma, alterações foram efetuadas que permitissem a criação de mapas térmicos sem sacrificar resolução do produto final, pois métodos típicos para processamento de imagens térmicas requerem uma área de captura maior, devido à falta de características invariantes neste tipo de imagens, o que leva a uma redução de resolução. Desta forma, o programa proposto foi desenvolvido através de bibliotecas open-source e os resultados foram comparados com modelos gerados através de software comerciais. Além do mais, devido à situação pandémica atual, não foi possível efetuar um estudo de campo para validar os modelos obtidos, como tal esta verificação foi feita através da correta localização geográfica do modelo, bem como avaliação visual dos modelos criados
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