发表机构
NVIDIA（NVIDIA）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自动驾驶3D模拟平台中神经模拟方法存在的速度慢和需逐场景调整问题，提出即时NuRec这一前馈神经重建模型，可快速将多视图驾驶日志转为3DGS世界，在数据集上PSNR表现出色，还能用于闭环模拟。
AI 中文摘要
3D模拟平台对自动驾驶至关重要，可实现端到端策略评估，降低开发成本并提高安全性。近年来神经模拟占主导，如NuRec等方法起核心作用，但仍较慢且需逐场景调整。本文提出即时NuRec，一种前馈神经重建模型，能将短多视图驾驶日志在单次前向传播中转换为完全可模拟的3D高斯点云（3DGS）世界。该模型接受校准相机装置的多视图输入，输出包括静态和动态3DGS层、天空立方体贴图及相机ISP校正，通过3DGUT支持非针孔相机模型。它能在约1.5秒内重建10 - 20秒的多相机场景，在Waymo开放数据集上PSNR比最强评估基线高2.01dB。即时NuRec深度集成到NuRec中，与AlpaSim兼容用于闭环模拟。
英文摘要
3D simulation platforms are critical for autonomous driving because they enable end-to-end policy evaluation, thereby reducing development costs and improving safety. In recent years, neural simulation has become predominant, with methods such as NuRec playing a central role; however, these methods remain relatively slow and typically require per-scene tuning. In this work, we present Instant NuRec, a feed-forward neural reconstruction model that turns a short multi-view driving log into a fully simulatable 3D Gaussian Splatting (3DGS) world in a single forward pass. The model accepts multi-view input from a calibrated camera rig and emits a layered output consisting of static and dynamic 3DGS layers, a sky cubemap, and per-camera ISP corrections, while providing native support for non-pinhole camera models via 3DGUT. It reconstructs a 10-20-second multi-camera scene in roughly 1.5 seconds and achieves a PSNR on the Waymo Open Dataset that is 2.01 dB above the strongest evaluated baseline. Instant NuRec is deeply integrated into NuRec and is compatible with AlpaSim for closed-loop simulation.
CommentsProject Page: https://research.nvidia.com/labs/sil/projects/instant-nurec/
