EVIS: A Physics-Grounded Event Camera Plugin for NVIDIA Isaac Sim

2026-07-09

AI Summary

This work addresses the scarcity of annotated real-world data and high acquisition costs that hinder the deployment of event cameras in robotics. To overcome these challenges, the authors develop a physically plausible event camera simulation plugin within NVIDIA Isaac Sim, grounded in the logarithmic intensity contrast model. The system incorporates bidirectional motion vector interpolation and a pixel-wise asynchronous reference update mechanism to generate high-frame-rate event streams with perfect ground truth. Designed for seamless integration, it enables rapid migration from conventional RGB cameras and offers optional noise injection and motion blur to better approximate real sensor characteristics. Leveraging GPU acceleration, the simulator achieves real-time performance, allowing a single GPU to efficiently produce high-quality synthetic data and substantially lower the barrier to developing event-based perception and control algorithms.

Abstract

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: https://github.com/spikelab-jhu/isaac-sim-event-camera-plugin

Problem

• event camera
• labeled data scarcity
• robotics
• simulated data generation
• perception and control

Innovation

• event camera
• physics simulation
• Isaac Sim
• real-time rendering
• domain gap