title: Distilling Diffusion Models to Efficient 3D LiDAR Scene Completion
summary: 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.
paper_authors._id: 675115a116e6038a2ac71a80
paper_authors.name: Shengyuan Zhang
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/abd2aae43e68c34770159c15a01c8297.svg
paper_authors.fullname: Shengyuan Zhang
paper_authors.type: user
paper_authors.user: SYZhang0805
paper_authors._id: 675115a116e6038a2ac71a81
paper_authors.name: An Zhao
paper_authors._id: 675115a116e6038a2ac71a82
paper_authors.name: Ling Yang
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/061a69d858b86d1600be916122cae7fc.svg
paper_authors.fullname: Ling Yang
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paper_authors.user: Lingaaaaaaa
paper_authors._id: 675115a116e6038a2ac71a83
paper_authors.name: Zejian Li
paper_authors._id: 675115a116e6038a2ac71a84
paper_authors.name: Chenye Meng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-05T10:25:41.733Z
paper_authors._id: 66d1fb162e0412fa2aa4ffb0
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paper_authors.fullname: Chenye Meng
paper_authors.type: user
paper_authors.user: mengcy
paper_authors._id: 675115a116e6038a2ac71a85
paper_authors.name: Haoran Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-05T09:44:09.643Z
paper_authors._id: 61384b860317b0a5c10877d3
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paper_authors.fullname: Haoran Xu
paper_authors.type: user
paper_authors.user: haoranxu
paper_authors._id: 675115a116e6038a2ac71a86
paper_authors.name: Tianrun Chen
paper_authors._id: 675115a116e6038a2ac71a87
paper_authors.name: AnYang Wei
paper_authors._id: 675115a116e6038a2ac71a88
paper_authors.name: Perry Pengyun GU
paper_authors._id: 675115a116e6038a2ac71a89
paper_authors.name: Lingyun Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-05T09:34:14.648Z
paper_authors._id: 662a5c0649e03000ed1adab4
paper_authors.avatarUrl: /avatars/961e4b9bf923923fcd871672d156f50c.svg
paper_authors.fullname: Lingyun Sun
paper_authors.type: user
paper_authors.user: slysun
paper_title: Distilling Diffusion Models to Efficient 3D LiDAR Scene Completion
paper_summary: 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.
paper_ai_keywords: diffusion models 3D LiDAR scene completion training stability completion quality sampling speed autonomous vehicles distillation method ScoreLiDAR Structural Loss scene-wise term point-wise term key landmark points relative configuration SemanticKITTI