LiDAR data exhibits significant domain gaps when collected from different devices or vehicles, even within the same driving environments. These gaps, akin to language barriers, hinder the synergistic use of diverse LiDAR datasets, limiting the scalability and unification of perception models. To address this challenge, we present the \textit{LiDAR Translator (LiT)}, a novel framework designed to unify LiDAR data into a single target ``language''. LiT represents a comprehensive system that integrates: a) generalizable scene modeling for foreground and background reconstruction; b) realistic LiDAR simulation with statistical and ray-drop modeling; c) a highly efficient ray casting engine accelerated on GPU. LiT enables efficient state-of-the-art zero-shot and unified domain detection capabilities across diverse LiDAR datasets, marking a significant step toward practical and efficient domain unification for LiDAR-based autonomous driving systems.Source code and demos are available at: https://anonymous-b6c5da.github.io/paper-4652.