3D Scene Generation: A Survey
Authors: name: Beichen Wen, name: Haozhe Xie, name: Zhaoxi Chen, name: Fangzhou Hong, name: Ziwei Liu

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.