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Статті в журналах з теми "3D object discovery"
Grinvald, Margarita, Fadri Furrer, Tonci Novkovic, Jen Jen Chung, Cesar Cadena, Roland Siegwart, and Juan Nieto. "Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery." IEEE Robotics and Automation Letters 4, no. 3 (July 2019): 3037–44. http://dx.doi.org/10.1109/lra.2019.2923960.
Повний текст джерелаK, Pramod, and Anjima AP. "Artificial Intelligence in 3D Bio Printing." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1577–86. http://dx.doi.org/10.22214/ijraset.2022.44161.
Повний текст джерелаPushkarev, A. A., O. V. Zaytceva, M. V. Vavulin, and 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 (June 15, 2016): 377–81. http://dx.doi.org/10.5194/isprs-archives-xli-b5-377-2016.
Повний текст джерелаPushkarev, A. A., O. V. Zaytceva, M. V. Vavulin, and 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 (June 15, 2016): 377–81. http://dx.doi.org/10.5194/isprsarchives-xli-b5-377-2016.
Повний текст джерелаChen, Yixin, and 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, no. 7 (July 6, 2024): 412. http://dx.doi.org/10.3390/biomimetics9070412.
Повний текст джерелаRaheja, Dev. "System Safety in Healthcare." Journal of System Safety 53, no. 1 (April 1, 2017): 12–14. http://dx.doi.org/10.56094/jss.v53i1.98.
Повний текст джерелаAsmatulu, Eylem, Rajakaruna A. D. N. V. Rajakaruna, Balakrishnan Subeshan, and M. Nizam Uddin. "3D printed superhydrophobic structures for sustainable manufacturing benefits: An overview." Journal of Management and Engineering Integration 15, no. 1 (June 2022): 45–56. http://dx.doi.org/10.62704/10057/24785.
Повний текст джерелаMiechowicz, Łukasz, Joanna Piątkowska-Małecka, Łukasz Maurycy Stanaszek, and Jakub Stępnik. ""Dom zmarłych” z Chodlika, gm. Karczmiska, woj. lubelskie. przyczynek do studiów nad grobami typu alt käbelich." Slavia Antiqua. Rocznik poświęcony starożytnościom słowiańskim, no. 63 (October 28, 2022): 153–78. http://dx.doi.org/10.14746/sa.2022.63.5.
Повний текст джерелаChoo, Yeon-Seung, Boeun Kim, Hyun-Sik Kim, and Yong-Suk Park. "Supervised Contrastive Learning for 3D Cross-Modal Retrieval." Applied Sciences 14, no. 22 (November 10, 2024): 10322. http://dx.doi.org/10.3390/app142210322.
Повний текст джерелаPoux, F., and 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 (August 12, 2020): 309–16. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-309-2020.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаThis 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
Payet, Nadia. "From shape-based object recognition and discovery to 3D scene interpretation." Thesis, 2011. http://hdl.handle.net/1957/21316.
Повний текст джерелаGraduation date: 2011
Access restricted to the OSU Community at author's request from May 12, 2011 - May 12, 2012
Книги з теми "3D object discovery"
Arcand, Kimberly, and Megan Watzke. Stars in Your Hand. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/13800.001.0001.
Повний текст джерелаЧастини книг з теми "3D object discovery"
Shin, Jiwon, Rudolph Triebel, and 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.
Повний текст джерелаBoudjoghra, Mohamed El Amine, Jean Lahoud, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan, and 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.
Повний текст джерелаHari Priya K and 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.
Повний текст джерелаBarth, 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, and 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.
Повний текст джерелаMouhamed, Mourad R., Ashraf Darwish, and 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.
Повний текст джерелаGross, Alan G., and 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.
Повний текст джерелаKosamiya, Vishvajitsinh, and 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.
Повний текст джерелаHak, 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.
Повний текст джерелаNakhaei, Mojdeh, Jing Ying Chong, Yunlong Tang, and 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.
Повний текст джерелаТези доповідей конференцій з теми "3D object discovery"
Srivastava, Siddharth, Gaurav Sharma, and 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.
Повний текст джерелаWang, Yuang, Xingyi He, Sida Peng, Haotong Lin, Hujun Bao, and 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.
Повний текст джерелаAbbeloos, Wim, Esra Ataer-Cansizoglu, Sergio Caccamo, Yuichi Taguchi, and 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.
Повний текст джерелаKarpathy, Andrej, Stephen Miller, and 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.
Повний текст джерелаMoosmann, Frank, and 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.
Повний текст джерелаNie, Neil, Samir Yitzhak Gadre, Kiana Ehsani, and 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.
Повний текст джерелаLiu, Bingyu, Yuhong Guo, Jianan Jiang, Jian Tang, and 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.
Повний текст джерелаAljaafreh, Mohammad, Haifa Raja Maamar, and 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.
Повний текст джерелаNoguchi, Atsuhiro, Umar Iqbal, Jonathan Tremblay, Tatsuya Harada, and 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.
Повний текст джерелаLee, Jae Hee, Matthias Kerzel, Kyra Ahrens, Cornelius Weber, and 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.
Повний текст джерела