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VisionSIM: A Physically Grounded Multi-Modal Simulator for Robot Perception

VisionSIM is a unified simulation platform that models the raw physical measurement process for diverse sensors including event, SPAD, iToF/dToF, and thermal cameras. It reproduces modality-specific failure modes such as LiDAR pileup, iToF phase wrapping, event camera refractory drops, and thermal history-dependent emission. The platform enables principled cross-modality comparison on identical scenes and includes a large indoor dataset with synchronized raw measurements and ground-truth annotations.

AI Brief

This work addresses a critical gap in robot perception simulation by modeling realistic sensor failure modes, which is valuable for researchers and engineers developing robust perception systems.

arXiv:2611.08368v1 Announce Type: cross 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 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.
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