FAQ from Rerun
What is Rerun?
Rerun is an SDK for logging computer vision and robotics data paired with a visualizer for exploring that data over time. It allows users to debug and understand the internal state and data of their systems with minimal code.
How to use Rerun?
To use Rerun, developers can log data to the Rerun SDK, which takes care of visualizing the data. Rerun handles live streams from multiple processes across the network and can also play back recordings. The Rerun Viewer creates configurable visualizations based on the logged data and allows users to scroll back and forth in time. Users can toggle between different timelines, such as log time and sensor time, to explore the data in multiple ways.
How do I use Rerun?
To use Rerun, developers can log data to the Rerun SDK, which takes care of visualizing the data. Rerun handles live streams from multiple processes across the network and can also play back recordings. The Rerun Viewer creates configurable visualizations based on the logged data and allows users to scroll back and forth in time. Users can toggle between different timelines, such as log time and sensor time, to explore the data in multiple ways.
What are the core features of Rerun?
The core features of Rerun include logging and visualization of computer vision and robotics data, live streams from multiple processes, playback of recorded data, automatic construction of visualizations with sane defaults, configurable visualizations based on data relationships, scrollable timeline for exploring data over time, support for different timelines, fast exploration, customizable toolkit for layout and shaders, embeddable views in other applications, flexible and portable Rust implementation, custom renderer using high-performance wgpu, and in-memory data store built on top of Arrow.
What are the use cases of Rerun?
Rerun is designed for computer vision and robotics applications. It can be used for debugging and understanding internal state and data of computer vision and robotics systems, visualizing and exploring computer vision and robotics data over time, analyzing and optimizing the performance of computer vision algorithms, monitoring and diagnosing issues in real-time computer vision and robotics applications, collaborative development and sharing of computer vision and robotics data, and building and testing computer vision and robotics applications with live data streams.