Diffusion Distillation With Direct Preference Optimization For Efficient 3D LiDAR Scene Completion

Understand

The application of diffusion models in 3D LiDAR scene completion is limited due to diffusion's slow sampling speed.

• Score distillation accelerates diffusion sampling but with performance degradation, while post-training with direct policy optimization (DPO) boosts performance using preference data.
• This paper proposes Distillation-DPO, a novel diffusion distillation framework for LiDAR scene completion with preference aligment.
• First, the student model generates paired completion scenes with different initial noises.