用激光雷达监督重建高速路场景，实现真实感渲染与激光数据合成。
LiHi-GS: LiDAR-Supervised Gaussian Splatting for Highway Driving Scene Reconstruction
- 基于激光雷达监督的高斯点云建模，提升高速场景重建精度。
- 在稀疏视角下实现亚厘米级深度误差，支持真实激光雷达渲染。
- 专为高速单调背景设计，适合自动驾驶仿真与数据增强需求。
真实感3D场景重建在自动驾驶中至关重要，可利用现有数据生成新场景以模拟危急情况，并无需额外采集成本扩展训练数据。高斯点云（Gaussian Splatting, GS）通过显式3D高斯表示，实现实时、真实感渲染，相比隐式神经辐射场（NeRF）处理更快且更易编辑。尽管已有大量GS研究在自动驾驶中取得进展，但忽视了两个关键问题：首先，现有方法主要聚焦低速、特征丰富的城市场景，忽略了高速场景在自动驾驶中的重要作用；其次，尽管激光雷达（LiDAR）广泛应用于自动驾驶平台，但现有方法主要依赖图像学习，仅将LiDAR用于初始估计或未建模精确传感器特性，未能充分利用其丰富的深度信息，限制了激光数据合成能力。本文提出一种新型GS方法——LiHi-GS，支持动态场景合成与编辑，通过激光雷达监督提升重建质量，并支持激光雷达渲染。不同于以往主要在城市数据集上测试的方法，据我们所知，这是首个专注于更具挑战性且高度相关的高速场景的研究，适用于稀疏视角和单调背景。项目主页：https://umautobots.github.io/lihi_gs
原文摘要 · Abstract (English)
Photorealistic 3D scene reconstruction plays an important role in autonomous driving, enabling the generation of novel data from existing datasets to simulate safety-critical scenarios and expand training data without additional acquisition costs. Gaussian Splatting (GS) facilitates real-time, photorealistic rendering with an explicit 3D Gaussian representation of the scene, providing faster processing and more intuitive scene editing than the implicit Neural Radiance Fields (NeRFs). While extensive GS research has yielded promising advancements in autonomous driving applications, they overlook two critical aspects: First, existing methods mainly focus on low-speed and feature-rich urban scenes and ignore the fact that highway scenarios play a significant role in autonomous driving. Second, while LiDARs are commonplace in autonomous driving platforms, existing methods learn primarily from images and use LiDAR only for initial estimates or without precise sensor modeling, thus missing out on leveraging the rich depth information LiDAR offers and limiting the ability to synthesize LiDAR data. In this paper, we propose a novel GS method for dynamic scene synthesis and editing with improved scene reconstruction through LiDAR supervision and support for LiDAR rendering. Unlike prior works that are tested mostly on urban datasets, to the best of our knowledge, we are the first to focus on the more challenging and highly relevant highway scenes for autonomous driving, with sparse sensor views and monotone backgrounds. Visit our project page at: https://umautobots.github.io/lihi_gs
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