VisionSIM 发布：面向机器人感知的多模态传感器仿真平台
VisionSIM: Beyond Idealized Sensors - A Multi-Modal Simulator for Robot Perception
做机器人感知仿真的可以看看这个 VisionSIM，它把 LiDAR、iToF、事件相机这些传感器的真实失效模式都模拟出来了，还开源了数据集。
VisionSIM 是一个多模态传感器仿真平台，通过建模原始物理测量过程来提升传感器真实感。平台能复现通用仿真器忽略的失效模式，包括 LiDAR 的 pileup 与 glare、iToF 相机的相位缠绕、饱和与运动伪影、事件相机的不应期丢失，以及热相机的历史依赖发射。该平台支持在相同场景与轨迹上做跨模态对比，并附带头部室内数据集，包含同步原始测量和真值标注。代码与数据集已开源发布。
VisionSIM: Beyond Idealized Sensors - A Multi-Modal Simulator for Robot Perception
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.