Bibliografias temáticas / 3D object discovery

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Literatura científica selecionada sobre o tema "3D object discovery"

Autor: Grafiati

Publicado: 8 de março de 2025

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Artigos de revistas sobre o assunto "3D object discovery"

Grinvald, Margarita, Fadri Furrer, Tonci Novkovic, Jen Jen Chung, Cesar Cadena, Roland Siegwart e Juan Nieto. "Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery". IEEE Robotics and Automation Letters 4, n.º 3 (julho de 2019): 3037–44. http://dx.doi.org/10.1109/lra.2019.2923960.

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K, Pramod, e Anjima AP. "Artificial Intelligence in 3D Bio Printing". International Journal for Research in Applied Science and Engineering Technology 10, n.º 6 (30 de junho de 2022): 1577–86. http://dx.doi.org/10.22214/ijraset.2022.44161.

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Abstract: 3D printing in full three-dimensional printing also known as additive manufacturing is the process of making three-dimensional solid objects from a digital file. The creation of a 3D printed object is achieved using additive processes. In an additive process, an object is created by laying down successive layers of material until the object is created. Each of these layers can be seen as a thinly sliced cross-section of the object.3D printing is the opposite of subtractive manufacturing which is cutting out / hollowing out a piece of metal or plastic with for instance a milling machine.3D printing enables you to produce complex shapes using less material than traditional manufacturing methods. As a result, 3D printing creates less material wastage. “3D Bio printing” or “bio printing” is a form of additive manufacturing that uses cells and biomaterials instead of traditional metals and plastics to create 3D constructs that are functional 3D tissues. But unlike 3D printing, bio printers print with cells and biomaterials, creating organ like structures that let living cells multiply. These biomaterials are called bio-inks, and they mimic the composition of our tissues. Bio printing can be applied to a variety of areas including but not limited to regenerative medicine, drug discovery and development, and 3D cell culture

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Pushkarev, A. A., O. V. Zaytceva, M. V. Vavulin e A. Y. Skorobogatova. "3D RECORDING OF A 19-CENTURY OB RIVER SHIP". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (15 de junho de 2016): 377–81. http://dx.doi.org/10.5194/isprs-archives-xli-b5-377-2016.

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A 3D recording of a 19-century wooden ship discovered on the bank of the river Ob (Western Siberia) was performed in autumn 2015. The archaeologized ship was partly under water, partly lying ashore, buried under fluvial deposits. The 3D recording was performed in October, when the water level was at its lowest after clearing the area around the ship. A 3D recording at the place of discovery was required as part of the ship museumification and reconstruction project. The works performed were primarily aimed at preserving as much information about the object as possible. <br><br> Given the location and peculiar features of the object, a combination of close-range photogrammetry and aerial photography was considered to be the best possible solution for creating a high-quality 3D model. <br><br> The dismantled ship was delivered to Nizhnevartovsk Museum of Local History in October 2015. The ship is going to be reassembled using the created 3D model to be exhibited in the museum. The resulting models are also going to be used to make a virtual 3D reconstruction of the ship in the future. We shot a stereoscopic video for Nizhnevartovsk Museum of Local History to let visitors see the place of discovery and explore the ship in greater details. Besides, 3D printing allowed for creating a miniature of the ship, which is also going to be included in the exposition devoted to this unique discovery.

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Chen, Yixin, e Qingnan Li. "Vehicle Behavior Discovery and Three-Dimensional Object Detection and Tracking Based on Spatio-Temporal Dependency Knowledge and Artificial Fish Swarm Algorithm". Biomimetics 9, n.º 7 (6 de julho de 2024): 412. http://dx.doi.org/10.3390/biomimetics9070412.

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In complex traffic environments, 3D target tracking and detection are often occluded by various stationary and moving objects. When the target is occluded, its apparent characteristics change, resulting in a decrease in the accuracy of tracking and detection. In order to solve this problem, we propose to learn the vehicle behavior from the driving data, predict and calibrate the vehicle trajectory, and finally use the artificial fish swarm algorithm to optimize the tracking results. The experiments show that compared with the CenterTrack method, the proposed method improves the key indicators of MOTA (Multi-Object Tracking Accuracy) in 3D object detection and tracking on the nuScenes dataset, and the frame rate is 26 fps.

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Raheja, Dev. "System Safety in Healthcare". Journal of System Safety 53, n.º 1 (1 de abril de 2017): 12–14. http://dx.doi.org/10.56094/jss.v53i1.98.

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A new technology, three-dimensional (3D) printing, has the potential to change the medical world. Objects are made by fusing or depositing materials, such as plastic, metal, powders, liquids or living cells, in layers to produce a 3D object. This technology started in manufacturing and was used to create spare parts for airplanes, eliminating the need for constructing manufacturing prototypes and producing new components within hours instead of weeks. The application of this technology in healthcare is growing. It is now used in the creation of customized prosthetics, implants and anatomical models. Its usage is expanding rapidly in other areas of healthcare, including pharmaceutical research regarding drug dosage forms, delivery and discovery.

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Asmatulu, Eylem, Rajakaruna A. D. N. V. Rajakaruna, Balakrishnan Subeshan e M. Nizam Uddin. "3D printed superhydrophobic structures for sustainable manufacturing benefits: An overview". Journal of Management and Engineering Integration 15, n.º 1 (junho de 2022): 45–56. http://dx.doi.org/10.62704/10057/24785.

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Superhydrophobic properties have been present in nature for many millennia before human beings discovered their true capabilities and utilized them to revolutionize modern societies. The most familiar form of hydrophobicity found in nature is that of the lotus leaf, where its ultra-low water adhesion and self-cleaning properties make it one of the best hydrophobic elements formed naturally. Since its discovery, artificially created superhydrophobic elements have been used in many industries --maritime, automobile, and medical -- due to their self-cleaning, antibacterial, and corrosion-prevention properties. However, for a surface to become superhydrophobic, it must possess a greater roughness. To achieve this, microscopic- or nanoscopic-level modifications must be made to the surface through various experimentations. For a surface to be considered superhydrophobic, it must have a water contact angle greater than 150°. One cost-effective method of manufacturing superhydrophobic materials is three-dimensional (3D) printing (additive manufacturing), which has been gaining popularity in the recent past. A 3D printing design is initially created using computer-aided design (CAD) software. Then, the design information is transferred to a 3D printer through digital slicing of the CAD design. 3D printing allows the printing of objects with various functionalities at pre-designed locations in the object, so it is important to investigate these phenomena. This paper provides an overview of several studies that were conducted to achieve superhydrophobicity through the 3D printing process. The following section of the manuscript includes an introduction, literature review, methods of increasing surface roughness for superhydrophobicity, market-available 3D printing materials, and their applications, discussion on 3D printing technologies and concluding remarks.

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Miechowicz, Łukasz, Joanna Piątkowska-Małecka, Łukasz Maurycy Stanaszek e Jakub Stępnik. ""Dom zmarłych” z Chodlika, gm. Karczmiska, woj. lubelskie. przyczynek do studiów nad grobami typu alt käbelich". Slavia Antiqua. Rocznik poświęcony starożytnościom słowiańskim, n.º 63 (28 de outubro de 2022): 153–78. http://dx.doi.org/10.14746/sa.2022.63.5.

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The article presents the results of archaeological research into an Alt Käbelich-type grave discovered in Chodlik, Karczmiska county, Lublin province. The pit contained layer burials of five individuals. The bone material included also horse remains. Other archaeological material contained fragments of clay utensils as well as pieces of metal and bone artefacts destroyed in fire. On the basis of 3D documentation, at attempt has been made to reconstruct the object by means of digital technology. The Chodlik discovery is a ontribution to the research into the occurrence of the Alt Käbelich type of graves and the concept of the so-called “house of the dead” in the Western Slavic Dominion.

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Choo, Yeon-Seung, Boeun Kim, Hyun-Sik Kim e Yong-Suk Park. "Supervised Contrastive Learning for 3D Cross-Modal Retrieval". Applied Sciences 14, n.º 22 (10 de novembro de 2024): 10322. http://dx.doi.org/10.3390/app142210322.

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Interoperability between different virtual platforms requires the ability to search and transfer digital assets across platforms. Digital assets in virtual platforms are represented in different forms or modalities, such as images, meshes, and point clouds. The cross-modal retrieval of three-dimensional (3D) object representations is challenging due to data representation diversity, making common feature space discovery difficult. Recent studies have been focused on obtaining feature consistency within the same classes and modalities using cross-modal center loss. However, center features are sensitive to hyperparameter variations, making cross-modal center loss susceptible to performance degradation. This paper proposes a new 3D cross-modal retrieval method that uses cross-modal supervised contrastive learning (CSupCon) and the fixed projection head (FPH) strategy. Contrastive learning mitigates the influence of hyperparameters by maximizing feature distinctiveness. The FPH strategy prevents gradient updates in the projection network, enabling the focused training of the backbone networks. The proposed method shows a mean average precision (mAP) increase of 1.17 and 0.14 in 3D cross-modal object retrieval experiments using ModelNet10 and ModelNet40 datasets compared to state-of-the-art (SOTA) methods.

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Poux, F., e J. J. Ponciano. "SELF-LEARNING ONTOLOGY FOR INSTANCE SEGMENTATION OF 3D INDOOR POINT CLOUD". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (12 de agosto de 2020): 309–16. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-309-2020.

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Abstract. Automation in point cloud data processing is central for efficient knowledge discovery. In this paper, we propose an instance segmentation framework for indoor buildings datasets. The process is built on an unsupervised segmentation followed by an ontology-based classification reinforced by self-learning. We use both shape-based features that only leverages the raw X, Y, Z attributes as well as relationship and topology between voxel entities to obtain a 3D structural connectivity feature describing the point cloud. These are then used through a planar-based unsupervised segmentation to create relevant clusters constituting the input of the ontology of classification. Guided by semantic descriptions, the object characteristics are modelled in an ontology through OWL2 and SPARQL to permit structural elements classification in an interoperable fashion. The process benefits from a self-learning procedure that improves the object description iteratively in a fully autonomous fashion. Finally, we benchmark the approach against several deep-learning methods on the S3DIS dataset. We highlight full automation, good performances, easy-integration and a precision of 99.99% for planar-dominant classes outperforming state-of-the-art deep learning.

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Mais fontes

Teses / dissertações sobre o assunto "3D object discovery"

Kara, Sandra. "Unsupervised object discovery in images and video data". Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASG019.

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Cette thèse explore les méthodes d'apprentissage auto-supervisé pour la localisation d'objets, communément appelées « Object Discovery ». La localisation d'objets dans les images et les vidéos est un élément essentiel des tâches de vision par ordinateur telles que la détection, la ré-identification, le suivi, etc. Les algorithmes supervisés actuels peuvent localiser (et classifier) les objets avec précision, mais ils sont coûteux en raison de la nécessité de données annotées. Le processus d'étiquetage est généralement répété pour chaque nouvelle donnée ou catégorie d'intérêt, limitant ainsi leur évolutivité. De plus, les approches sémantiquement spécialisées nécessitent une connaissance préalable des classes cibles, restreignant leur utilisation aux objets connus. La découverte d'objets vise à pallier ces limitations en étant plus générique. La première contribution de la thèse s'est concentrée sur la modalité image, en étudiant comment les caractéristiques des modèles transformers de vision auto-supervisés peuvent servir d'indices pour la découverte d'objets multiples. Afin de localiser les objets dans leur définition la plus large, nous avons étendu notre étude aux données vidéo, en exploitant les indices de mouvement et en ciblant la localisation d'objets capables de se déplacer. Nous avons introduit la modélisation de l'arrière-plan et la distillation de connaissances dans la découverte d'objets pour résoudre le problème de la sur-segmentation de l'arrière-plan dans les méthodes existantes, et pour réintégrer les objets statiques, améliorant ainsi de manière significative le rapport signal/bruit dans les prédictions. Reconnaissant les limites des données à modalité unique, nous avons incorporé des données 3D à travers un apprentissage par distillation de connaissances cross-modale. L'échange de connaissances entre les domaines 2D et 3D a permis d'améliorer l'alignement des régions d'objets entre les deux modalités, rendant possible l'utilisation de la cohérence multi-modale comme critère de confiance
This thesis explores self-supervised learning methods for object localization, commonly known as Object Discovery. Object localization in images and videos is an essential component of computer vision tasks such as detection, re-identification, tracking etc. Current supervised algorithms can localize (and classify) objects accurately but are costly due to the need for annotated data. The process of labeling is typically repeated for each new data or category of interest, limiting their scalability. Additionally, the semantically specialized approaches require prior knowledge of the target classes, restricting their use to known objects. Object Discovery aims to address these limitations by being more generic. The first contribution of this thesis focused on the image modality, investigating how features from self-supervised vision transformers can serve as cues for multi-object discovery. To localize objects in their broadest definition, we extended our focus to video data, leveraging motion cues and targeting the localization of objects that can move. We introduced background modeling and knowledge distillation in object discovery to tackle the background over-segmentation issue in existing object discovery methods and to reintegrate static objects, significantly improving the signal-to-noise ratio in predictions. Recognizing the limitations of single-modality data, we incorporated 3D data through a cross-modal distillation framework. The knowledge exchange between 2D and 3D domains improved alignment on object regions between the two modalities, enabling the use of multi-modal consistency as a confidence criterion

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Payet, Nadia. "From shape-based object recognition and discovery to 3D scene interpretation". Thesis, 2011. http://hdl.handle.net/1957/21316.

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This dissertation addresses a number of inter-related and fundamental problems in computer vision. Specifically, we address object discovery, recognition, segmentation, and 3D pose estimation in images, as well as 3D scene reconstruction and scene interpretation. The key ideas behind our approaches include using shape as a basic object feature, and using structured prediction modeling paradigms for representing objects and scenes. In this work, we make a number of new contributions both in computer vision and machine learning. We address the vision problems of shape matching, shape-based mining of objects in arbitrary image collections, context-aware object recognition, monocular estimation of 3D object poses, and monocular 3D scene reconstruction using shape from texture. Our work on shape-based object discovery is the first to show that meaningful objects can be extracted from a collection of arbitrary images, without any human supervision, by shape matching. We also show that a spatial repetition of objects in images (e.g., windows on a building facade, or cars lined up along a street) can be used for 3D scene reconstruction from a single image. The aforementioned topics have never been addressed in the literature. The dissertation also presents new algorithms and object representations for the aforementioned vision problems. We fuse two traditionally different modeling paradigms Conditional Random Fields (CRF) and Random Forests (RF) into a unified framework, referred to as (RF)^2. We also derive theoretical error bounds of estimating distribution ratios by a two-class RF, which is then used to derive the theoretical performance bounds of a two-class (RF)^2. Thorough experimental evaluation of individual aspects of all our approaches is presented. In general, the experiments demonstrate that we outperform the state of the art on the benchmark datasets, without increasing complexity and supervision in training.
Graduation date: 2011
Access restricted to the OSU Community at author's request from May 12, 2011 - May 12, 2012

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Livros sobre o assunto "3D object discovery"

Arcand, Kimberly, e Megan Watzke. Stars in Your Hand. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/13800.001.0001.

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An illustrated guide to exploring the Universe in three dimensions. Astronomers have made remarkable discoveries about our Universe, despite their reliance on the flat projection, or 2D view, the sky has offered them. But now, drawing on the vast stores of data available from telescopes and observatories on the ground and in space, astronomers can now use visualization tools to explore the cosmos in 3D. In Stars in Your Hand, Kimberly Arcand and Megan Watzke offer an illustrated guide to exploring the Universe in three dimensions, with easy-to-follow instructions for creating models of stars and constellations using a 3D printer and 3D computer imaging. Stars in Your Hand and 3D technology make learning about space an adventure. Intrigued by the stunning images from high-powered telescopes? Using this book, you can fly virtually through a 3D spacescape and hold models of cosmic objects in your hand. Arcand and Watzke outline advances in 3D technology, describe some amazing recent discoveries in astronomy, reacquaint us with the night sky, and provide brief biographies of the telescopes, probes, and rovers that are bringing us so much data. They then offer images and instructions for printing and visualizing stars, nebulae, supernovae, galaxies, and even black holes in 3D. The 3D Universe is a marvel, and Stars in Your Hand serves as a unique and thrilling portal to discovery.

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Capítulos de livros sobre o assunto "3D object discovery"

Shin, Jiwon, Rudolph Triebel e Roland Siegwart. "Unsupervised 3D Object Discovery and Categorization for Mobile Robots". In Springer Tracts in Advanced Robotics, 61–76. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29363-9_4.

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Boudjoghra, Mohamed El Amine, Jean Lahoud, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan e Fahad Shahbaz Khan. "Continual Learning and Unknown Object Discovery in 3D Scenes via Self-distillation". In Lecture Notes in Computer Science, 416–31. Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-73464-9_25.

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Hari Priya K e Malathi S. "Efficient Face Mask Recognition System by Using Deep Learning Methodology". In Advances in Parallel Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210047.

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In this project, mask Recognition System is presented, that utilizes the prospect of Object Detection, completed the assistance of Deep Learning philosophies. The past work gives the topic of article identification by joining coarse-grained and fine-grained discovery philosophies without precedent for police work the moving items on high goal recordings. Period object location and acknowledgment finds careful applications in various fields like clinical applications, security police examination, and independent vehicles. There unit of estimation a few machine and profound learning procedures that unit utilized for object discovery and acknowledgment. The development of a convolutional neural organization (CNN) has given a major forward leap to protest discovery and acknowledgment. Convolutional Neural Network (CNN) has arrived at the exemplification of picture characterization for different application. Explicitly in 2D-CNN, there’s huge advancement for object discovery, beside 3D-CNN, it’s still toward the start of partner time. Profound CNN’s unit acclimated get extra exact directions and to deal with high goal video outlines. By taking this idea of location on the grounds that the base to our work, the framework includes in perceiving the essence of the individual and checks if the face have mask or not, befittingly guaranteeing the individual complies with the assurance safety measures so as that the unfurl of hepatotoxic infection may be managed. The framework will unpredictably be utilized for future face ID with face mask till the pandemic gets died.

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Barth, E., G. A. Manfrim, L. E. Silva, A. L. R. Correia, C. B. Bennemann, M. F. Martin, J. G. Faria, W. G. Fiirst, S. S. F. Souza e F. P. A. Lima. "3D VIRTUAL TOUR AT IFMT TANGARÁ DA SERRA: APPLICATION OF VIRTUAL REALITY AS A TEACHING TOOL". In Open Science Research XVI, 11–27. Editora Científica Digital, 2024. http://dx.doi.org/10.37885/240717273.

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This work aims to develop a virtual tour of the IFMT Tangará da Serra campus facilities using 3D modeling and Virtual Reality (VR) to be applied in the discipline of introduction to Computer Graphics of the technical course of Maintenance and Support in Informatics integrated to High School . VR allows the interaction and navigation of users in 3D environments maintained by computer, using mapping channels and analysis of user behavior, enabling the exchange of information between the virtual environment and the user, affecting one or more human senses. It is an interaction and entertainment technology that can be successfully applied to assist in teaching strategies in high school subjects. With the evolution of education, that is, the improvement of the processes of exploration, discovery, observation and construction of knowledge, new teaching tools have been emerging, among which virtual learning environments stand out because they allow people to conduct experiments or perform tasks in a new and different way that they could not do in the physical/real world, such as flying, visiting places that do not exist or are difficult to access through the manipulation and analysis of the object of study itself. Thus, VR will be fundamental in this process of educational evolution, where the use of digital tools such as ICTs (Information and Communication Technologies) is increasingly sought for the successful application of active teaching methodologies in the classroom. Therefore, the objective of this project refers to the development of an interactive VR system to be used with 3D VR glasses, designed, modeled and rendered using the free software Blender, to present a virtual tour of the facilities of the IFMT Tangará da Serra campus . This tool will be very useful for demonstrating computer graphics concepts to students, as well as to the external community, as a form of institutional promotion and marketing.

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Mouhamed, Mourad R., Ashraf Darwish e Aboul Ella Hassanien. "2D and 3D Intelligent Watermarking". In Handbook of Research on Machine Learning Innovations and Trends, 652–69. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2229-4.ch028.

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These days the enormous advancement in the field of data innovation and the wide employments of the web made the security of the information confront a major issue to accomplish the information assurance. The authentication and the copyright of the information is a critical piece of this issue. Scientists started to discover answers for this issue, watermarking and cryptology two of these solutions. Digital watermarking refers to the process of embedding imperceptible information called a digital watermark into a cover multimedia object so that the information may be detected or extracted later for security purposes. Cover multimedia object used to hide watermark information can be any digital media that we used in our daily life for data distribution such as: audio, 2D images, 3D images, and video. The problem that face the researchers in developing a watermarking techniques that the trade of between the impeccability and the robustness of the watermark this chapter focus on how the intelligent algorithms can help in this issue. This chapter surveys the watermarking techniques in 2D and 3D techniques. We conclude that watermarking technique are efficient for different areas of applications.

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Gross, Alan G., e Joseph E. Harmon. "Archival Websites in the Humanities and Sciences". In The Internet Revolution in the Sciences and Humanities. Oxford University Press, 2016. http://dx.doi.org/10.1093/oso/9780190465926.003.0009.

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A South African by birth, white, of German ancestry, fluent in Afrikaans, Helena Pohlandt-McCormick spent six months in her native country in 1993 and a full year in 1994 studying the Soweto uprising. During that time, she assiduously examined the relevant archives but was unable to find any of the posters she knew the marching students carried: . . . From the transcripts and correspondence of the Cillié Commission I knew that the Commission had received, from the police, many posters and banners that had been confiscated during various student marches in 1976. None of them would have fit into a traditional archive document box and, though mentioned on the list of evidence associated with the Cillié Commission, they were initially not to be found. I continued to request that archivists search the repositories—without success. Until, one day, perhaps exasperated by my persistence or wanting to finally prove to me that there was nothing to be found in the space associated with K345, the archival designator of my Soweto materials, one of the archivists relented and asked me to accompany her into the vaults in order to help her search for these artifacts of the uprising! To be sure, there were no posters to be found in the shelf space that housed the roughly nine hundred boxes of evidence associated with the Cillié Commission. But then, as my disappointed eyes swept the simultaneously ominous and tantalizing interior of the vault, I saw a piece of board protruding over the topmost edge of the shelf. There, almost 9 feet into the air, in the shadowy space on top of the document shelves, lay a pile of posters and banners. . . . We can understand Pohlandt-McCormick’s mounting sense of excitement. It is not just the discovery itself; it is the sense of being in touch with the past—literally in touch. It is the knowledge that no photograph can do justice to any 3D object, whether it is a collection of posters, a cache of cold fusion memorabilia, or Enrico Fermi’s Nobel medal.

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Kosamiya, Vishvajitsinh, e Jing Wang. "High-k, Low-Loss Ceramic-Thermoplastic Composite Feedstock Filaments for Fused Deposition Modeling of Microwave and mm-Wave Devices". In Additive Manufacturing - Present and Sustainable Future, Materials and Applications [Working Title]. IntechOpen, 2025. https://doi.org/10.5772/intechopen.1008537.

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Maturing of additive manufacturing (AM) techniques has increased their utilization for fabricating radio frequency (RF) and microwave devices. Solid composites used in material extrusion AM have experienced considerable expansion over the past decade, incorporating functional properties into 3D-printed objects. There are encouraging indications from AM material research that electrically efficient AM materials can be discovered. These materials would be useful for producing microwave components in the future. One of the enabling techniques for fabricating these materials is to incorporate nano/microparticles or fillers into thermoplastic material. Composite material 3D printing is a novel approach to managing materials’ microwave properties. While extrinsic qualities (effective permittivity) can be controlled by shape and porosity management, intrinsic attributes are tied to the composition of composites. Furthermore, combining various materials to increase the spectrum of available microwave characteristics is made possible by multi-material 3D printing. In this chapter, we explore different methodologies to fabricate ceramic/thermoplastic composites for fused deposition modeling (FDM) of RF and microwave devices. Analytical models for predicting effective permittivity of the composite are discussed and application examples of FDM printed RF, microwave and mm-wave devices employing composites are presented.

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Hak, Jonathan W. "Image-Based Evidence as a Didactic Tool". In Image-Based Evidence in International Criminal Prosecutions, 34–58. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780198889533.003.0003.

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Abstract This chapter explores the role that image-based evidence serves in achieving important goals of international criminal justice, the most important of which is truth discovery. International criminal prosecutions also rule upon evidence that forms part of the historical record of conflict. Visual evidence plays a significant role in ascertaining truth and documenting conflict because it provides a level of depth and detail that cannot necessarily be achieved solely through words. This chapter examines the use of image-based evidence as a didactic tool to aid the court in comprehending the probative value of tendered evidence. It discusses the use of timelines to provide a longitudinal chronology of a case; the use of terrestrial laser scanning to accurately measure crime scenes, areas of interest, and objects to create a 3D depiction; and computer generated visualizations in the form of animations and simulations for use as demonstrative evidence.

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Nakhaei, Mojdeh, Jing Ying Chong, Yunlong Tang e Shahnaz Mansouri. "Plant-Based Sustainable Self-Cleaners in Nanotechnology Era: From Mechanism to Assembling". In Nature-Inspired Self-Cleaning Surfaces in Nanotechnology Era [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.111966.

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Nature has always been a resource of inspiration for humans, providing valuable lessons that have led to innovative solutions throughout history. Observing the micro-nano roughness structure of bio-surfaces has led to the discovery of natural self-cleaning surfaces for over 25 years. This has sparked a new field of research with valuable applications. Numerous self-cleaning products made from plant extracts have been created by replicating the natural purifying abilities of plant surfaces. Significant literature exists on the development, classification, extraction, and production of self-cleaning agents for diverse industries through a thorough understanding of bio-cleaning mechanisms. Various methods have been developed to synthesize these surfaces, including immersion, electrochemical deposition, emulsion, electrospinning, phase-separation, Chemical-Vapor-Deposition (CVD), spray coating, wet chemical reaction, and three-dimensional printing (3D-printing), among others. Currently, the primary objective is to gain knowledge from nature and utilize it to develop novel products for food, pharmaceutical, and related industries. Natural plant-based self-cleaning surfaces can be characterized by their superhydrophobicity and superhydrophilicity regimes. The process of 3D-printing is a computer-based technique that builds up three-dimensional objects through the layer-by-layer deposition of materials. The creation of effective self-cleaning surfaces with unique wettability, chemical properties, and microstructure depends on the design and engineering of solid surfaces.

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Trabalhos de conferências sobre o assunto "3D object discovery"

Srivastava, Siddharth, Gaurav Sharma e Brejesh Lall. "Large Scale Novel Object Discovery in 3D". In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018. http://dx.doi.org/10.1109/wacv.2018.00026.

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Wang, Yuang, Xingyi He, Sida Peng, Haotong Lin, Hujun Bao e Xiaowei Zhou. "AutoRecon: Automated 3D Object Discovery and Reconstruction". In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.02048.

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Abbeloos, Wim, Esra Ataer-Cansizoglu, Sergio Caccamo, Yuichi Taguchi e Yukiyasu Domae. "3D Object Discovery and Modeling Using Single RGB-D Images Containing Multiple Object Instances". In 2017 International Conference on 3D Vision (3DV). IEEE, 2017. http://dx.doi.org/10.1109/3dv.2017.00056.

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Karpathy, Andrej, Stephen Miller e Li Fei-Fei. "Object discovery in 3D scenes via shape analysis". In 2013 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2013. http://dx.doi.org/10.1109/icra.2013.6630857.

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Moosmann, Frank, e Miro Sauerland. "Unsupervised discovery of object classes in 3D outdoor scenarios". In 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE, 2011. http://dx.doi.org/10.1109/iccvw.2011.6130365.

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Nie, Neil, Samir Yitzhak Gadre, Kiana Ehsani e Shuran Song. "Structure from Action: Learning Interactions for 3D Articulated Object Structure Discovery". In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2023. http://dx.doi.org/10.1109/iros55552.2023.10342135.

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Liu, Bingyu, Yuhong Guo, Jianan Jiang, Jian Tang e Weihong Deng. "Multi-view Correlation based Black-box Adversarial Attack for 3D Object Detection". In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467432.

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Aljaafreh, Mohammad, Haifa Raja Maamar e Azzedine Boukerche. "An efficient object discovery and selection protocol in 3D streaming-based systems over thin mobile devices". In 2013 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2013. http://dx.doi.org/10.1109/wcnc.2013.6554935.

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Noguchi, Atsuhiro, Umar Iqbal, Jonathan Tremblay, Tatsuya Harada e Orazio Gallo. "Watch It Move: Unsupervised Discovery of 3D Joints for Re-Posing of Articulated Objects". In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.00366.

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Lee, Jae Hee, Matthias Kerzel, Kyra Ahrens, Cornelius Weber e Stefan Wermter. "What is Right for Me is Not Yet Right for You: A Dataset for Grounding Relative Directions via Multi-Task Learning". In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/145.

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Understanding spatial relations is essential for intelligent agents to act and communicate in the physical world. Relative directions are spatial relations that describe the relative positions of target objects with regard to the intrinsic orientation of reference objects. Grounding relative directions is more difficult than grounding absolute directions because it not only requires a model to detect objects in the image and to identify spatial relation based on this information, but it also needs to recognize the orientation of objects and integrate this information into the reasoning process. We investigate the challenging problem of grounding relative directions with end-to-end neural networks. To this end, we provide GRiD-3D, a novel dataset that features relative directions and complements existing visual question answering (VQA) datasets, such as CLEVR, that involve only absolute directions. We also provide baselines for the dataset with two established end-to-end VQA models. Experimental evaluations show that answering questions on relative directions is feasible when questions in the dataset simulate the necessary subtasks for grounding relative directions. We discover that those subtasks are learned in an order that reflects the steps of an intuitive pipeline for processing relative directions.

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