Diffusion Distillation With Direct Preference Optimization For Efficient
  3D LiDAR Scene Completion
Authors: name: An Zhaol, name: Shengyuan Zhang, name: Ling Yang, name: Zejian Li, name: Jiale Wu, name: Haoran Xu, name: AnYang Wei, name: Perry Pengyun GU Lingyun Sun

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. Second, using
LiDAR scene evaluation metrics as preference, we construct winning and losing
sample pairs. Such construction is reasonable, since most LiDAR scene metrics
are informative but non-differentiable to be optimized directly. Third,
Distillation-DPO optimizes the student model by exploiting the difference in
score functions between the teacher and student models on the paired completion
scenes. Such procedure is repeated until convergence. Extensive experiments
demonstrate that, compared to state-of-the-art LiDAR scene completion diffusion
models, Distillation-DPO achieves higher-quality scene completion while
accelerating the completion speed by more than 5-fold. Our method is the first
to explore adopting preference learning in distillation to the best of our
knowledge and provide insights into preference-aligned distillation. Our code
is public available on https://github.com/happyw1nd/DistillationDPO.