title: 3D Scene Generation: A Survey
summary: 3D scene generation seeks to synthesize spatially structured, semantically
  meaningful, and photorealistic environments for applications such as immersive
  media, robotics, autonomous driving, and embodied AI. Early methods based on
  procedural rules offered scalability but limited diversity. Recent advances in
  deep generative models (e.g., GANs, diffusion models) and 3D representations
  (e.g., NeRF, 3D Gaussians) have enabled the learning of real-world scene
  distributions, improving fidelity, diversity, and view consistency. Recent
  advances like diffusion models bridge 3D scene synthesis and photorealism by
  reframing generation as image or video synthesis problems. This survey provides
  a systematic overview of state-of-the-art approaches, organizing them into four
  paradigms: procedural generation, neural 3D-based generation, image-based
  generation, and video-based generation. We analyze their technical foundations,
  trade-offs, and representative results, and review commonly used datasets,
  evaluation protocols, and downstream applications. We conclude by discussing
  key challenges in generation capacity, 3D representation, data and annotations,
  and evaluation, and outline promising directions including higher fidelity,
  physics-aware and interactive generation, and unified perception-generation
  models. This review organizes recent advances in 3D scene generation and
  highlights promising directions at the intersection of generative AI, 3D
  vision, and embodied intelligence. To track ongoing developments, we maintain
  an up-to-date project page:
  https://github.com/hzxie/Awesome-3D-Scene-Generation.
paper_authors._id: 681d7b5ae27a030c96a28bde
paper_authors.name: Beichen Wen
paper_authors.status: admin_assigned
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paper_authors.fullname: Wen Beichen
paper_authors.type: user
paper_authors.user: wenbc21
paper_authors._id: 681d7b5ae27a030c96a28bdf
paper_authors.name: Haozhe Xie
paper_authors.status: admin_assigned
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paper_authors.fullname: Haozhe Xie
paper_authors.type: user
paper_authors.user: hzxie
paper_authors._id: 681d7b5ae27a030c96a28be0
paper_authors.name: Zhaoxi Chen
paper_authors.status: admin_assigned
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paper_authors._id: 62fc8cf7ee999004b5a8b982
paper_authors.avatarUrl: /avatars/6c5dda9e58747054a989f077a078f3dc.svg
paper_authors.fullname: Zhaoxi Chen
paper_authors.type: user
paper_authors.user: FrozenBurning
paper_authors._id: 681d7b5ae27a030c96a28be1
paper_authors.name: Fangzhou Hong
paper_authors._id: 681d7b5ae27a030c96a28be2
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-09T08:57:27.473Z
paper_authors._id: 62ab1ac1d48b4d8b048a3473
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
paper_authors.fullname: Ziwei Liu
paper_authors.type: user
paper_authors.user: liuziwei7
paper_title: 3D Scene Generation: A Survey
paper_summary: 3D scene generation seeks to synthesize spatially structured, semantically
  meaningful, and photorealistic environments for applications such as immersive
  media, robotics, autonomous driving, and embodied AI. Early methods based on
  procedural rules offered scalability but limited diversity. Recent advances in
  deep generative models (e.g., GANs, diffusion models) and 3D representations
  (e.g., NeRF, 3D Gaussians) have enabled the learning of real-world scene
  distributions, improving fidelity, diversity, and view consistency. Recent
  advances like diffusion models bridge 3D scene synthesis and photorealism by
  reframing generation as image or video synthesis problems. This survey provides
  a systematic overview of state-of-the-art approaches, organizing them into four
  paradigms: procedural generation, neural 3D-based generation, image-based
  generation, and video-based generation. We analyze their technical foundations,
  trade-offs, and representative results, and review commonly used datasets,
  evaluation protocols, and downstream applications. We conclude by discussing
  key challenges in generation capacity, 3D representation, data and annotations,
  and evaluation, and outline promising directions including higher fidelity,
  physics-aware and interactive generation, and unified perception-generation
  models. This review organizes recent advances in 3D scene generation and
  highlights promising directions at the intersection of generative AI, 3D
  vision, and embodied intelligence. To track ongoing developments, we maintain
  an up-to-date project page:
  https://github.com/hzxie/Awesome-3D-Scene-Generation.
paper_ai_summary: This survey examines the state-of-the-art methods in 3D scene generation, categorizing them into procedural, neural 3D, image-based, and video-based paradigms, and discusses current challenges and future directions.
paper_ai_keywords: deep generative models GANs diffusion models NeRF 3D Gaussians procedural generation neural 3D-based generation image-based generation video-based generation generative AI 3D vision embodied intelligence