Abstract: Diffusion models have been applied to 3D LiDAR scene completion due to their strong training stability and high completion quality.However, the slow sampling speed limits the practical application of diffusion-based scene completion models since autonomous vehicles require an efficient perception of surrounding environments. This paper proposes a novel distillation method tailored for 3D LiDAR scene completion models, dubbed $\textbf{ScoreLiDAR}$, which achieves efficient yet high-quality scene completion.ScoreLiDAR enables the distilled model to sample in significantly fewer steps after distillation.To improve completion quality, we also introduce a novel $\textbf{Structural Loss}$, which encourages the distilled model to capture the geometric structure of the 3D LiDAR scene.The loss contains a scene-wise term constraining the holistic structure and a point-wise term constraining the key landmark points and their relative configuration.Extensive experiments demonstrate that ScoreLiDAR significantly accelerates the completion time from 30.55 to 5.37 seconds per frame ($>$5$\times$) on SemanticKITTI and achieves superior performance compared to state-of-the-art 3D LiDAR scene completion models.