LiDAR Scene Synthesis with Triplane Diffusion Models via LiDAR Simulation-based Coarse-to-Fine Point Sampling

Realistic LiDAR scene synthesis is critical for autonomous driving perception systems, yet existing methods struggle to simultaneously capture the distinctive scanning patterns, geometric structures, and semantic properties of real-world data. To tackle these challenges, we present the first two-stage framework, called LiTDM, that leverages triplane representation as a latent space proxy for diffusion models tailored to LiDAR scene synthesis. First, a triplane-based autoencoder is trained to predict point-based occupancy and intensity with a novel ray sampling strategy. Then, we sample the diffusion model trained on triplane features to generate realistic LiDAR scenes. With triplane features as a proxy, our method achieves pattern realism, geometric accuracy, and semantic coherence. Moreover, our proposed LiDAR Simulation-based Point Sampling (LSPS) ensures the characteristic scanning patterns of real sensors. Experimental results show that our framework generates highly realistic LiDAR scenes against the baselines. Furthermore, the flexibility of LiTDM extends beyond unconditional synthesis, enabling diverse conditional generation tasks such as camera-to-LiDAR and text-to-LiDAR generation. Code will be released to support reproducibility.