VisionSIM: Beyond Idealized Sensors - A Multi-Modal Simulator for Robot Perception
VisionSIM: Beyond Idealized Sensors - A Multi-Modal Simulator for Robot Perception
This paper introduces VisionSIM, a unified simulation platform designed to improve the realism of robotic perception by modeling the raw physical measurement processes for various conventional and emerging sensor modalities. Unlike existing simulators that idealize sensors, VisionSIM exposes fine-grained control over physical parameters to reproduce critical failure modes specific to each modality, such as pileup and glare for LiDAR, phase wrapping and saturation for iToF, refractory drops for event cameras, and history-dependent emission for thermal cameras. The platform enables principled cross-modality comparison on identical scenes and trajectories. Additionally, the authors release the platform along with a large indoor dataset featuring synchronized raw measurements and rich ground-truth annotations.
The authors present VisionSIM, a multi-modal simulation platform that models raw physical measurement processes for diverse sensors including cameras, event cameras, LiDAR, iToF, and thermal cameras to expose specific failure modes such as pileup, glare, phase wrapping, and refractory drops.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Environment
- SimulationOpen source S2
Dataset details reported by the source
- Modalities
- raw physical measurement process for diverse conventional and emerging modalities Open source S3
- Embodiments
- indoor dataset with synchronized raw measurements and rich ground-truth annotations Open source S4
- dataset: Reported pending Open source S4
- code: not_reported
- weights: not_reported
- paper: not_reported
- dataset: not_reported
What remains unknown
Dataset licensing and commercial-use rights are not established here.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
arXiv:2611.08368v1 Announce Type: new Abstract: 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 sen
Open source S2
sor 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 emissio
Open source S3
n 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.
Open source S4
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Source：arXiv Robotics — research abstracts · arxiv.org