基于高斯泼溅（Gaussian Splatting）的工业级传感器仿真：面向可扩展编辑与全栈验证的模块化框架
Industrial-Grade Sensor Simulation via Gaussian Splatting: A Modular Framework for Scalable Editing and Full-Stack Validation
AI总结：
本文提出基于Gaussian Splatting的模块化工业级传感器仿真框架，通过2D神经高斯表示、基元库编辑和可控扩散扩展，实现低延迟、高一致性及自动驾驶全栈验证。
摘要：
传感器仿真对于自动驾驶系统的可扩展验证至关重要，但现有基于神经辐射场（Neural Radiance Fields，NeRF）的方法在工业工作流程中面临适用性和效率方面的挑战。本文介绍一种基于高斯泼溅（Gaussian Splatting，GS）的系统以应对这些挑战：我们首先拆解传感器仿真器的组成，并分析GS相对于NeRF可能具有的优势。随后在实践中，我们利用GS的显式场景表示和实时渲染能力，重构三个关键组件：（1）选择2D神经高斯表示，用于符合物理规律的场景与传感器建模；（2）提出一套场景编辑流程，利用高斯基元库进行数据增强；（3）结合可控扩散模型，用于场景扩展与和谐化。我们在一个支持相机和LiDAR传感器的专有自动驾驶数据集上实现该框架。通过消融研究，我们证明本方法降低了逐帧仿真延迟，实现了更好的几何与光度一致性，并支持可解释的显式场景编辑和扩展。此外，我们展示了将这种基于GS的传感器仿真器与交通仿真器和动态仿真器集成，如何实现端到端自动驾驶算法的全栈测试。我们的工作同时提供算法洞见和实践验证，确立GS作为工业级传感器仿真基石的地位。
英文摘要：
Sensor simulation is pivotal for scalable validation of autonomous driving systems, yet existing Neural Radiance Fields (NeRF) based methods face applicability and efficiency challenges in industrial workflows. This paper introduces a Gaussian Splatting (GS) based system to address these challenges: We first break down sensor simulator components and analyze the possible advantages of GS over NeRF. Then in practice, we refactor three crucial components through GS, to leverage its explicit scene representation and real-time rendering: (1) choosing the 2D neural Gaussian representation for physics-compliant scene and sensor modeling, (2) proposing a scene editing pipeline to leverage Gaussian primitives library for data augmentation, and (3) coupling a controllable diffusion model for scene expansion and harmonization. We implement this framework on a proprietary autonomous driving dataset supporting cameras and LiDAR sensors. We demonstrate through ablation studies that our approach reduces frame-wise simulation latency, achieves better geometric and photometric consistency, and enables interpretable explicit scene editing and expansion. Furthermore, we showcase how integrating such a GS-based sensor simulator with traffic and dynamic simulators enables full-stack testing of end-to-end autonomy algorithms. Our work provides both algorithmic insights and practical validation, establishing GS as a cornerstone for industrial-grade sensor simulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
