VisionSIM：超越理想化传感器——面向机器人感知的多模态模拟器
VisionSIM: Beyond Idealized Sensors - A Multi-Modal Simulator for Robot Perception
Sacha Jungerman, Kaustubh Sadekar, Avery Gump, Sriram Narayanan, Shantanu Gupta, Bhuyashi Deka, Srinivasa Narasimhan, Atul Ingle 等 9 位作者
AI 速览：提出统一且物理建模的仿真平台 VisionSIM，对相机及事件、SPAD、iToF/dToF、热成像等新兴模态的原始测量过程建模，以复现真实传感器失效模式并支持跨模态对比。
Robotic perception increasingly relies on heterogeneous sensor suites combining cameras with emerging modalities (event, SPAD, iToF/dToF, and thermal). Yet existing simulators idealize these sensors, hiding critical modality-specific failure modes that limit real-world deployment. We present VisionSIM, a unified, physically grounded simulation platform that elevates sensor realism to a first-class concern by modeling the raw physical measurement process for diverse conventional and emerging modalities. We expose fine-grained control over physical sensor parameters, reproducing failure modes overlooked by general-purpose simulators: pileup and glare for LiDAR; phase wrapping, saturation, and motion artifacts for iToF; refractory drops for event cameras; and history-dependent emission for thermal cameras. This enables principled cross-modality comparison on identical scenes and trajectories. We release the platform and a large indoor dataset with synchronized raw measurements and rich ground-truth annotations.