High-quality Image-based Interactive Exploration of Real-World Environments 1

By M. Uyttendaele, Antonio Criminisi, Sing Bing Kang, Simon A. J. Winder, Richard I. Hartley, Richard Szeliski. 2003

328 citations recorded by the source corpus.

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Citations in the corpus

The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards (2018, background): “As Kelley et al. made clear in their work on Privacy Nutrition Label [22] , design is a key element in the efficacy of the label.”
Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence (2020, background): “proposed standardised “nutrition label” notices with icons representing the type of data collected and how it is used, and showed how it helped users find information more quickly and accurately [34].”
Your Location has been Shared 5,398 Times!: A Field Study on Mobile App Privacy Nudging (2015, background): “This observation has led to alternate proposals for presenting privacy risks to users in a manner that is more usable and salient [24].”
Robust Rotation and Translation Estimation in Multiview Reconstruction (2007, unknown): “It would be extremely difﬁcult to ﬁnd out that this EG does not exist based on the two images only. translations using them [26, 15].”
Explainability fact sheets: a framework for systematic assessment of explainable approaches (2019, background): “Kelley et al. [19] argued for a similar concept (“nutrition labels for privacy”) to assess privacy of systems that handle personal (and sensitive) information.”
Standardizing privacy notices: an online study of the nutrition label approach (2010, background): “We previously proposed a privacy “nutrition label” to assist consumer understanding of privacy policies [6].”
Privacy as part of the app decision-making process (2013, background): “Research in privacy policies, financial privacy notices, and access control have all similarly shown that privacy-related concepts and terms are often not well understood by users expected to make privacy decisions [13, 15, 22].”
ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles (2019, background): “…industry [4] or nutrition labels from the food industry [7], the suggested templates vary widely in length and appearance, ranging from a single concise page of succinct statements [11] or a set of symbols and visualizations [7, 10] to upwards of 10 pages of detailed prose and graphs [4].”