LiDAR-GS++:Improving LiDAR Gaussian Reconstruction via Diffusion Priors

TL;DR

LiDAR-GS++ introduces a diffusion prior-enhanced LiDAR Gaussian Splatting method that improves real-time, high-fidelity 3D scene reconstruction and novel view synthesis, especially in extrapolated regions.

Contribution

It presents a novel diffusion prior-based reconstruction approach that extends LiDAR Gaussian Splatting to achieve global geometric consistency and better extrapolation.

Findings

Achieves state-of-the-art performance on public datasets.

Improves extrapolated view synthesis quality.

Ensures geometric consistency in reconstructed scenes.

Abstract

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.