title: Diffusion-Based Generative Models for 3D Occupancy Prediction in
    Autonomous Driving
summary: Accurately predicting 3D occupancy grids from visual inputs is critical for
  autonomous driving, but current discriminative methods struggle with noisy
  data, incomplete observations, and the complex structures inherent in 3D
  scenes. In this work, we reframe 3D occupancy prediction as a generative
  modeling task using diffusion models, which learn the underlying data
  distribution and incorporate 3D scene priors. This approach enhances prediction
  consistency, noise robustness, and better handles the intricacies of 3D spatial
  structures. Our extensive experiments show that diffusion-based generative
  models outperform state-of-the-art discriminative approaches, delivering more
  realistic and accurate occupancy predictions, especially in occluded or
  low-visibility regions. Moreover, the improved predictions significantly
  benefit downstream planning tasks, highlighting the practical advantages of our
  method for real-world autonomous driving applications.
paper_authors._id: 6842afaa6b5d1e675f254cf1
paper_authors.name: Yunshen Wang
paper_authors._id: 6842afaa6b5d1e675f254cf2
paper_authors.name: Yicheng Liu
paper_authors._id: 6842afaa6b5d1e675f254cf3
paper_authors.name: Tianyuan Yuan
paper_authors._id: 6842afaa6b5d1e675f254cf4
paper_authors.name: Yucheng Mao
paper_authors._id: 6842afaa6b5d1e675f254cf5
paper_authors.name: Yingshi Liang
paper_authors._id: 6842afaa6b5d1e675f254cf6
paper_authors.name: Xiuyu Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-07T05:48:41.444Z
paper_authors._id: 634aab35dcf125e4dafc87b1
paper_authors.avatarUrl: /avatars/aa2db84fd423e9eefe3ef3167c9d3999.svg
paper_authors.fullname: YangXiuyu
paper_authors.type: user
paper_authors.user: gzzyyxy
paper_authors._id: 6842afaa6b5d1e675f254cf7
paper_authors.name: Honggang Zhang
paper_authors._id: 6842afaa6b5d1e675f254cf8
paper_authors.name: Hang Zhao
paper_title: Diffusion-Based Generative Models for 3D Occupancy Prediction in
    Autonomous Driving
paper_summary: Accurately predicting 3D occupancy grids from visual inputs is critical for
  autonomous driving, but current discriminative methods struggle with noisy
  data, incomplete observations, and the complex structures inherent in 3D
  scenes. In this work, we reframe 3D occupancy prediction as a generative
  modeling task using diffusion models, which learn the underlying data
  distribution and incorporate 3D scene priors. This approach enhances prediction
  consistency, noise robustness, and better handles the intricacies of 3D spatial
  structures. Our extensive experiments show that diffusion-based generative
  models outperform state-of-the-art discriminative approaches, delivering more
  realistic and accurate occupancy predictions, especially in occluded or
  low-visibility regions. Moreover, the improved predictions significantly
  benefit downstream planning tasks, highlighting the practical advantages of our
  method for real-world autonomous driving applications.
paper_ai_summary: Diffusion models improve 3D occupancy prediction from visual inputs, enhancing accuracy and robustness in complex and occluded scenes, which benefits autonomous driving.
paper_ai_keywords: diffusion models generative modeling 3D occupancy grids autonomous driving noise robustness 3D scene priors downstream planning tasks