Distilling Diffusion Models to Efficient 3D LiDAR Scene Completion
Diffusion models have been applied to 3D LiDAR scene completion due to their
strong training stability and high completion quality. However, the slow
sampling speed limits the practical application of diffusion-based scene
completion models since autonomous vehicles require an efficient perception of
surrounding environments. This paper proposes a novel distillation method
tailored for 3D LiDAR scene completion models, dubbed ScoreLiDAR,
which achieves efficient yet high-quality scene completion. ScoreLiDAR enables
the distilled model to sample in significantly fewer steps after distillation.
To improve completion quality, we also introduce a novel Structural
Loss, which encourages the distilled model to capture the geometric structure
of the 3D LiDAR scene. The loss contains a scene-wise term constraining the
holistic structure and a point-wise term constraining the key landmark points
and their relative configuration. Extensive experiments demonstrate that
ScoreLiDAR significantly accelerates the completion time from 30.55 to 5.37
seconds per frame (>5times) on SemanticKITTI and achieves superior
performance compared to state-of-the-art 3D LiDAR scene completion models. Our
code is publicly available at https://github.com/happyw1nd/ScoreLiDAR.
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Shengyuan Zhang
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2024-12-05T10:25:31.664Z
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Shengyuan Zhang
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An Zhao
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Ling Yang
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2025-02-11T15:34:15.544Z
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Ling Yang
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Zejian Li
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Chenye Meng
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2024-12-05T10:25:41.733Z
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Chenye Meng
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Haoran Xu
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2024-12-05T09:44:09.643Z
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Haoran Xu
user
haoranxu
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Tianrun Chen
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AnYang Wei
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Perry Pengyun GU
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Lingyun Sun
admin_assigned
2024-12-05T09:34:14.648Z
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Lingyun Sun
user
slysun
Distilling Diffusion Models to Efficient 3D LiDAR Scene Completion
Diffusion models have been applied to 3D LiDAR scene completion due to their
strong training stability and high completion quality. However, the slow
sampling speed limits the practical application of diffusion-based scene
completion models since autonomous vehicles require an efficient perception of
surrounding environments. This paper proposes a novel distillation method
tailored for 3D LiDAR scene completion models, dubbed ScoreLiDAR,
which achieves efficient yet high-quality scene completion. ScoreLiDAR enables
the distilled model to sample in significantly fewer steps after distillation.
To improve completion quality, we also introduce a novel Structural
Loss, which encourages the distilled model to capture the geometric structure
of the 3D LiDAR scene. The loss contains a scene-wise term constraining the
holistic structure and a point-wise term constraining the key landmark points
and their relative configuration. Extensive experiments demonstrate that
ScoreLiDAR significantly accelerates the completion time from 30.55 to 5.37
seconds per frame (>5times) on SemanticKITTI and achieves superior
performance compared to state-of-the-art 3D LiDAR scene completion models. Our
code is publicly available at https://github.com/happyw1nd/ScoreLiDAR.
A novel distillation method, ScoreLiDAR, accelerates 3D LiDAR scene completion with high-quality results by reducing sampling steps and incorporating a Structural Loss to maintain geometric structure.
diffusion models 3D LiDAR scene completion training stability sampling speed autonomous vehicles distillation method Structural Loss scene-wise term point-wise term SemanticKITTI