Scene Dreamer
SceneDreamer is an AI tool that utilizes an unconditional generative model to synthesize large-scale 3D landscapes from random noises. Unlike other tools, SceneDreamer does not require any 3D annotations and learns from in-the-wild 2D image collections. The tool’s core components include an efficient and expressive 3D scene representation, a generative scene parameterization, and an effective renderer that leverages knowledge from 2D images.
At the heart of SceneDreamer is an efficient bird’s-eye-view (BEV) representation generated from simplex noise. This representation consists of a height field and a semantic field. The height field represents the surface elevation of 3D scenes, while the semantic field provides detailed scene semantics. This BEV scene representation allows SceneDreamer to represent a 3D scene with quadratic complexity, disentangle geometry and semantics, and perform efficient training.
SceneDreamer introduces a novel generative neural hash grid to parameterize the latent space based on 3D positions and scene semantics. This approach aims to encode generalizable features across scenes and align content. By utilizing this parameterization, SceneDreamer can generate vivid and diverse unbounded 3D worlds.
To produce photorealistic images, SceneDreamer employs a neural volumetric renderer that is learned from 2D image collections through adversarial training. This renderer ensures that the generated scenes are visually appealing and realistic. In comparison to state-of-the-art methods, SceneDreamer demonstrates superior performance in generating diverse and lifelike unbounded 3D worlds.
In summary, SceneDreamer is an AI tool that excels in generating large-scale 3D landscapes without the need for 3D annotations. Its efficient scene representation, generative parameterization, and neural volumetric renderer enable the synthesis of vivid and diverse unbounded 3D worlds.