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CVPR 2022 Open Access Repository
Source: openaccess.thecvf.com
Article summary:
1. Block-NeRF is a variant of Neural Radiance Fields that can represent large-scale environments.
2. Architectural changes have been made to make NeRF robust to data captured over months under different environmental conditions.
3. A grid of Block-NeRFs has been built from 2.8 million images to create the largest neural scene representation to date, capable of rendering an entire neighborhood of San Francisco.
Article analysis:
The article is generally reliable and trustworthy, as it provides detailed information on the research conducted and the results obtained. The authors provide evidence for their claims in the form of diagrams and figures, which support their findings and conclusions. Furthermore, they cite relevant sources throughout the article, which adds credibility to their work.
However, there are some potential biases in the article that should be noted. For example, the authors focus mainly on the advantages of Block-NeRF without exploring any potential drawbacks or limitations that may exist with this technology. Additionally, they do not discuss any possible risks associated with using this technology or how it could be misused by malicious actors. Finally, while they cite relevant sources throughout the article, they do not present both sides equally when discussing certain topics such as scalability and robustness; instead they focus mainly on how these features benefit Block-NeRF without exploring any potential drawbacks or limitations that may exist with them as well.