3D Scene Generation: A Survey
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.
681d7b5ae27a030c96a28bde
Beichen Wen
admin_assigned
2025-05-09T08:56:55.900Z
672392c4a4c4381cefc06416
/avatars/8ee84a7e3e91e5d13074bc3c407ff75d.svg
Wen Beichen
user
wenbc21
681d7b5ae27a030c96a28bdf
Haozhe Xie
admin_assigned
2025-05-09T08:57:02.341Z
63f47b5321eb234ab739e91a
https://cdn-avatars.huggingface.co/v1/production/uploads/63f47b5321eb234ab739e91a/vWfFNVtMkHl8gieha5PPd.jpeg
Haozhe Xie
user
hzxie
681d7b5ae27a030c96a28be0
Zhaoxi Chen
admin_assigned
2025-05-09T08:57:08.543Z
62fc8cf7ee999004b5a8b982
/avatars/6c5dda9e58747054a989f077a078f3dc.svg
Zhaoxi Chen
user
FrozenBurning
681d7b5ae27a030c96a28be1
Fangzhou Hong
681d7b5ae27a030c96a28be2
Ziwei Liu
admin_assigned
2025-05-09T08:57:27.473Z
62ab1ac1d48b4d8b048a3473
https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
Ziwei Liu
user
liuziwei7
3D Scene Generation: A Survey
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.
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.
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