发表机构
Johns Hopkins University（约翰霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对事件相机数据收集难题，提出基于物理的EVIS插件用于NVIDIA Isaac Sim，能在模拟器内生成带标签事件流，通过特定模型和可配置设置实现实时单GPU生成，其流可供预训练网络用于下游任务。
AI 中文摘要
事件相机具有微秒级时间分辨率、低延迟和高动态范围，对机器人技术很有吸引力。然而，特定机器人和场景的带标签事件相机数据稀缺且收集成本高，阻碍了基于事件的感知和控制发展。我们提出了EVIS，这是一个用于NVIDIA Isaac Sim的基于物理的事件相机插件，可在物理模拟器内直接生成高速、完全带标签的事件流。该插件实现了具有逐像素异步参考更新的忠实对数强度对比度事件模型，只需少量更改就能从普通RGB相机迁移并集成到任何Isaac Sim/Isaac Lab场景中，继承模拟器的物理特性和帧完美的地面真值。它完全可配置，提供仅渲染稀疏关键帧并通过双向运动向量变形合成中间帧的插值选项，实现单GPU实时生成。可选的传感器噪声和运动模糊进一步缩小了与真实相机的差距。生成的流可直接供预训练的事件网络用于下游任务。
英文摘要
Event cameras are increasingly adopted in embodied perception for their microsecond temporal resolution, high dynamic range, and resilience to motion blur. However, training event-based models for robotics requires large-scale, action-conditioned data with dense physical annotations that are difficult to collect in the real world. We introduce EVIS, an open-source physics-grounded event simulator integrated into NVIDIA Isaac Sim that generates events from linear-HDR radiance from a closed-loop robot training episode. Rather than relying on learning-based video interpolation or expensive dense rendering, EVIS exploits renderer-provided motion vectors and depth maps through bi-directional warping and depth-based splatting. This enables high throughput, real-time, and high-fidelity event generation. We evaluate EVIS across runtime efficiency, sim-to-real transfer, and zero-shot model compatibility. A rotation-speed estimator trained solely on EVIS events achieves 2.75 rad/s MAE on real sensor data. Pretrained models for reconstruction, matching, and tracking perform competitively on EVIS events without any fine-tuning. EVIS sustains real-time generation across GPU-parallel environments on a single GPU. Code repository: https://github.com/spikelab-jhu/isaac-sim-event-camera-plugin.
