---
title: "VisionSIM: Beyond Idealized Sensors - A Multi-Modal Simulator for Robot Perception"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.08368v1"
permalink: "https://feed.astralor.com/items/itm_3bfa9ef44bb37e9c95a1755b"
published: "2026-11-05T17:48:00.000Z"
score: 57
channel: "tech"
tags: ["多模态","VisionSIM","Beyond Idealized Sensors","Multi-Modal Simulator","Robot Perception Robotic","SPAD","Yet","Robotic"]
---

# VisionSIM: Beyond Idealized Sensors - A Multi-Modal Simulator for Robot Perception

## 速览

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 mod…

## 关键点

- 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 modal…

## 全文

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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