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Joint 2D-3D-Semantic Data for Indoor Scene Understanding [arXiv]-All Databases
Source: webofscience.com
Article summary:
1. This article presents a dataset of large-scale indoor spaces that provides a variety of mutually registered modalities from 2D, 2.5D and 3D domains, with instance-level semantic and geometric annotations.
2. The dataset covers over 6,000 m2and contains over 102,000 RGB images, along with the corresponding depths, surface normals, semantic annotations, global XYZ images (all in forms of both regular and 360oequirectangular images) as well as camera information.
3. The dataset enables development of joint and cross-modal learning models and potentially unsupervised approaches utilizing the regularities present in large-scale indoor spaces.
Article analysis:
The article is generally reliable and trustworthy due to its clear presentation of data sources and methods used for collecting the data. It also provides detailed information on the various modalities included in the dataset such as RGB images, depths, surface normals, semantic annotations etc., which makes it easier to understand how the data was collected and what it can be used for. Additionally, the article includes citation network information from Inspec® which further adds to its credibility.
However, there are some potential biases that should be noted when considering this article's trustworthiness. For example, while it does provide detailed information on the various modalities included in the dataset, it does not provide any details on how these modalities were chosen or why they were considered important for understanding indoor scenes. Additionally, while it does mention potential applications for this dataset such as joint and cross-modal learning models or unsupervised approaches utilizing regularities present in large-scale indoor spaces; it does not provide any evidence or examples to support these claims or explore possible counterarguments that could arise from using this data set for such purposes. Furthermore, there is no discussion about possible risks associated with using this data set or any other ethical considerations that should be taken into account when using this data set for research purposes.
In conclusion, while this article is generally reliable and trustworthy due to its clear presentation of data sources and methods used for collecting the data; there are some potential biases that should be noted when considering its trustworthiness such as lack of evidence supporting claims made about potential applications of this dataset or exploration of possible counterarguments that could arise from using this data set for such purposes as well as lack of discussion about possible risks associated with using this data set or any other ethical considerations that should be taken into account when using this data set for research purposes.
Topics for further research:
Ethical considerations for using large-scale indoor datasets
Risks associated with using large-scale indoor datasets
Joint and cross-modal learning models for indoor scenes
Unsupervised approaches for large-scale indoor spaces
Evidence for potential applications of large-scale indoor datasets
Counterarguments for using large-scale indoor datasets