SimULi: 基于无散射变换的实时激光雷达和摄像头模拟
SimULi: Real-Time LiDAR and Camera Simulation with Unscented Transforms
AI总结：
SimULi 提出一种实时渲染任意相机模型和激光雷达数据的方法，通过扩展 3DGUT 并引入自动化拼接策略和射线剔除，提升了渲染速度和精度，适用于自动驾驶场景的高保真测试。
摘要：
严格测试自主机器人，如自动驾驶车辆，对于确保其在现实部署中的安全性至关重要。这需要构建高保真的模拟器来测试超出现实世界安全或全面收集的场景。现有的基于 NeRF 和 3DGS 的神经渲染方法具有潜力，但存在渲染速度低或只能渲染针孔相机模型的问题，限制了其在需要高畸变镜头和激光雷达数据的应用中的适用性。多传感器模拟带来了额外的挑战，因为现有方法通过牺牲其他模态的质量来优先考虑某一模态的质量。为克服这些限制，我们提出了 SimULi，这是首个能够实时渲染任意相机模型和激光雷达数据的方法。我们的方法通过扩展原生支持复杂相机模型的 3DGUT，并通过自动化拼接策略和基于射线的剔除来引入激光雷达支持。为解决跨传感器不一致问题，我们设计了一种因子化的 3D 高斯表示和锚定策略，将平均相机和深度误差减少了高达 40% 相比现有方法。SimULi 的渲染速度比光线追踪方法快 10-20 倍，比先前的光栅化工作快 1.5-10 倍（并支持更广泛的相机模型）。在两个广泛基准测试的自动驾驶数据集上评估时，SimULi 在众多相机和激光雷达指标上与现有最先进方法相匹配或超越。
英文摘要：
Rigorous testing of autonomous robots, such as self-driving vehicles, is essential to ensure their safety in real-world deployments. This requires building high-fidelity simulators to test scenarios beyond those that can be safely or exhaustively collected in the real-world. Existing neural rendering methods based on NeRF and 3DGS hold promise but suffer from low rendering speeds or can only render pinhole camera models, hindering their suitability to applications that commonly require high-distortion lenses and LiDAR data. Multi-sensor simulation poses additional challenges as existing methods handle cross-sensor inconsistencies by favoring the quality of one modality at the expense of others. To overcome these limitations, we propose SimULi, the first method capable of rendering arbitrary camera models and LiDAR data in real-time. Our method extends 3DGUT, which natively supports complex camera models, with LiDAR support, via an automated tiling strategy for arbitrary spinning LiDAR models and ray-based culling. To address cross-sensor inconsistencies, we design a factorized 3D Gaussian representation and anchoring strategy that reduces mean camera and depth error by up to 40% compared to existing methods. SimULi renders 10-20x faster than ray tracing approaches and 1.5-10x faster than prior rasterization-based work (and handles a wider range of camera models). When evaluated on two widely benchmarked autonomous driving datasets, SimULi matches or exceeds the fidelity of existing state-of-the-art methods across numerous camera and LiDAR metrics.
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。
