Neural radiance fields (NeRFs) are advanced machine learning techniques that can generate three-dimensional (3D) representations of objects or environments from two-dimensional (2D) images. As these techniques can model complex real-world environments realistically and in detail, they could greatly support robotics research.
Most existing datasets and platforms for training NeRFs, however, are designed to be used offline, as they require the completion of a pose optimization step that significantly delays the creation of photo realistic representations. This has so far prevented most roboticists from using these techniques to test their algorithms on physical robots in real-time.
A research team at Stanford University recently introduced NerfBridge, a new open-source software package for training NeRF algorithms that could ultimately enable their use in online robotics experiments, This package, introduced in a paper pre-published on arXiv, is designed to effectively bridge ROS (the robot operating system), a renowned software library for robotics applications, and Nerfstudio, an open-source library designed to train NeRFs in real-time.
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