用扩散模型补全激光雷达扫描缺失区域，提升全景重建质量
LiDAR-GS++:Improving LiDAR Gaussian Reconstruction via Diffusion Priors
- 引入可控扩散生成模型，根据粗略渲染结果补全几何一致的扫描数据
- 在多个公开数据集上实现插值与外推视角的最佳性能
- 适合自动驾驶场景下的高保真三维重建与实时重模拟
基于GS的渲染近期在激光雷达领域取得显著进展，其在质量和速度上均超越神经辐射场（NeRF）。然而，这些方法在单次扫描下进行视图外推时会出现伪影。为此，我们提出LiDAR-GS++，一种通过扩散先验增强的激光雷达高斯点云重建方法，支持公共城市道路场景下的实时、高保真重模拟。具体而言，我们设计了一种可控的激光雷达生成模型，以粗略外推渲染为条件生成额外的几何一致扫描数据，并采用高效的蒸馏机制实现大范围重建。该方法扩展了欠拟合区域的重建能力，确保外推视图的全局几何一致性，同时保留传感器捕捉的精细表面细节。在多个公开数据集上的实验表明，LiDAR-GS++在插值与外推视角上均达到当前最优性能，优于现有的GS与NeRF基方法。
原文摘要 · Abstract (English)
Recent GS-based rendering has made significant progress for LiDAR, surpassing Neural Radiance Fields (NeRF) in both quality and speed. However, these methods exhibit artifacts in extrapolated novel view synthesis due to the incomplete reconstruction from single traversal scans. To address this limitation, we present LiDAR-GS++, a LiDAR Gaussian Splatting reconstruction method enhanced by diffusion priors for real-time and high-fidelity re-simulation on public urban roads. Specifically, we introduce a controllable LiDAR generation model conditioned on coarsely extrapolated rendering to produce extra geometry-consistent scans and employ an effective distillation mechanism for expansive reconstruction. By extending reconstruction to under-fitted regions, our approach ensures global geometric consistency for extrapolative novel views while preserving detailed scene surfaces captured by sensors. Experiments on multiple public datasets demonstrate that LiDAR-GS++ achieves state-of-the-art performance for both interpolated and extrapolated viewpoints, surpassing existing GS and NeRF-based methods.
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