title: Diffusion Distillation With Direct Preference Optimization For Efficient
    3D LiDAR Scene Completion
summary: 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.
paper_authors._id: 67ff1026f8afab940cc23f88
paper_authors.name: An Zhaol
paper_authors._id: 67ff1026f8afab940cc23f89
paper_authors.name: Shengyuan Zhang
paper_authors._id: 67ff1026f8afab940cc23f8a
paper_authors.name: Ling Yang
paper_authors._id: 67ff1026f8afab940cc23f8b
paper_authors.name: Zejian Li
paper_authors._id: 67ff1026f8afab940cc23f8c
paper_authors.name: Jiale Wu
paper_authors._id: 67ff1026f8afab940cc23f8d
paper_authors.name: Haoran Xu
paper_authors._id: 67ff1026f8afab940cc23f8e
paper_authors.name: AnYang Wei
paper_authors._id: 67ff1026f8afab940cc23f8f
paper_authors.name: Perry Pengyun GU Lingyun Sun
paper_title: Diffusion Distillation With Direct Preference Optimization For Efficient
    3D LiDAR Scene Completion
paper_summary: 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.
paper_ai_summary: Distillation-DPO accelerates 3D LiDAR scene completion by integrating score distillation with preference learning, achieving higher quality and faster completion.
paper_ai_keywords: diffusion models score distillation direct policy optimization DPO student model teacher model LiDAR scene completion preference alignment scene evaluation metrics score functions