Diffusion Distillation With Direct Preference Optimization For Efficient
  3D LiDAR Scene Completion
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.
67ff1026f8afab940cc23f88
An Zhaol
67ff1026f8afab940cc23f89
Shengyuan Zhang
67ff1026f8afab940cc23f8a
Ling Yang
claimed_verified
2025-06-27T08:58:19.730Z
64fde4e252e82dd432b74ce9
https://cdn-avatars.huggingface.co/v1/production/uploads/64fde4e252e82dd432b74ce9/-CQZbBP7FsPPyawYrsi4z.jpeg
Ling Yang
user
Lingaaaaaaa
67ff1026f8afab940cc23f8b
Zejian Li
67ff1026f8afab940cc23f8c
Jiale Wu
67ff1026f8afab940cc23f8d
Haoran Xu
67ff1026f8afab940cc23f8e
AnYang Wei
67ff1026f8afab940cc23f8f
Perry Pengyun GU Lingyun Sun
Diffusion Distillation With Direct Preference Optimization For Efficient
  3D LiDAR Scene Completion
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.
Distillation-DPO accelerates 3D LiDAR scene completion by integrating score distillation with preference learning, achieving higher quality and faster completion.
diffusion models score distillation direct policy optimization DPO student model teacher model LiDAR scene completion preference alignment scene evaluation metrics score functions