3D Scene Generation: A Survey

Understand

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.