EVIS: A Physics-Grounded Event Camera Plugin for NVIDIA Isaac Sim
Quick Answer
EVIS is a physics-grounded event camera plugin for NVIDIA Isaac Sim that generates high-rate, fully labeled event streams, facilitating event-based perception in robotics.
Quick Take
EVIS is a physics-grounded event camera plugin for NVIDIA Isaac Sim that generates high-rate, fully labeled event streams, facilitating event-based perception in robotics. It features a log-intensity contrast event model, real-time generation on a single GPU, and is compatible with existing Isaac Sim scenes, making it a valuable tool for developers facing data scarcity in event-camera applications.
Key Points
- Generates high-rate event streams directly in NVIDIA Isaac Sim.
- Implements a log-intensity contrast event model with asynchronous updates.
- Supports real-time generation on a single GPU with interpolation options.
- Integrates seamlessly into any Isaac Sim scene, inheriting physics.
- Generated streams are usable by pretrained event networks for various tasks.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Event cameras offer microsecond temporal resolution, low latency, and high dynamic range, making them attractive for robotics. However, labeled event-camera data for a specific robot and scene is scarce and expensive to collect, which slows the development of event-based perception and control. We present EVIS: a physics-grounded event camera plugin for NVIDIA Isaac Sim that generates high-rate, fully labeled event streams directly inside a physics simulator. The plugin implements a faithful log-intensity contrast event model with per-pixel asynchronous reference updates; it migrates from a normal RGB camera with few changes and integrates into any Isaac Sim / Isaac Lab scene, inheriting the simulator's physics and frame-perfect ground truth. It is fully configurable, and offers an interpolation option that renders only sparse keyframes and synthesizes the in-between frames through bidirectional motion-vector warping, making real-time generation on a single GPU possible. Optional sensor noise and motion blur further narrow the gap to real cameras. The generated streams are directly usable by pretrained event networks for downstream tasks. Code repository: this https URL
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO) |
| Cite as: | arXiv:2607.08098 [cs.CV] |
| (or arXiv:2607.08098v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.08098 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Linli Shi [view email]
[v1]
Thu, 9 Jul 2026 04:13:31 UTC (16,911 KB)
— Originally published at arxiv.org
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