Many Steps, One Pass: Latent Trajectory Simulation for Efficient LiDAR Scene Completion

Diffusion-based LiDAR scene completion unlocks the potential to transform sparse scans into rich, detailed 3D geometry. However, traditional iterative denoising requires invoking the full denoising backbone at every step, burdening inference with substantial computational cost. While some distillation methods aims to streamline this process, most of which reduces the nuanced evolution of reconstruction to a single endpoint, discarding the valuable intermediate states. To achieve a better trade-off between accuracy and efficiency, we introduce LiTE (LiDAR Trajectory Evolution), which performs multiple teacher-aligned latent updates at a deep feature interface within each student transition, decoupling internal geometric evolution from repeated full-backbone evaluation. Within this framework, we propose Visibility-Conditioned Latent Transport (VCLT) to execute these latent updates while maintaining robust local scan conditioning as the trajectory unfolds; it dynamically updates scan-to-point correspondences at the feature level and leverages sensor-ray visibility to precisely guide geometric refinement. Comprehensive experiments on SemanticKITTI and KITTI-360 shows that LiTE achieves state-of-the-art Chamfer distance and JSD, while delivers an $18.4\times$ inference speedup over the 50-step LiDiff teacher and $5.9\times$ improvement over 8-step ScoreLiDAR.