SceneDreamer is an unconditional generative model for unbounded 3D scenes that can synthesize large-scale 3D landscapes from random noises. The framework is learned from in-the-wild 2D image collections without any 3D annotations. At the core of the tool is a principled learning paradigm comprising an efficient and expressive 3D scene representation, a generative scene parameterization, and an effective renderer that leverages the knowledge from 2D images. SceneDreamer employs an efficient bird’s-eye-view (BEV) representation generated from simplex noise, which 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. The BEV scene representation enables the tool to represent a 3D scene with quadratic complexity, disentangle geometry and semantics, and perform efficient training. The tool proposes a novel generative neural hash grid to parameterize the latent space given 3D positions and the scene semantics, which aims to encode generalizable features across scenes and align content. Lastly, a neural volumetric renderer, learned from 2D image collections through adversarial training, is employed to produce photorealistic images. SceneDreamer is effective in generating vivid and diverse unbounded 3D worlds and is superior to state-of-the-art methods in this regard.
More details about Scene Dreamer
What purpose does the height field serve in SceneDreamer?
The height field in SceneDreamer represents the surface elevation of 3D scenes. This is a crucial component of the BEV scene representation as it allows SceneDreamer to model the physical height differences within a given 3D landscape.
What is the BEV scene representation in SceneDreamer?
In SceneDreamer, the BEV (Bird’s Eye View) scene representation is generated from simplex noise. It includes two fields – a height field and a semantic field. These are used to represent the surface elevation of 3D scenes and provide detailed scene semantics respectively.
What’s the significance of the style code in SceneDreamer?
The style code in SceneDreamer is part of the input to the model. In conjunction with a simplex noise, the style code enables the synthesis of a variety of large-scale 3D scenes where the camera can move freely and get realistic renderings.
What does the term ‘unbounded 3D scene’ mean in the context of SceneDreamer?
‘Unbounded 3D scene’ in the context of SceneDreamer refers to the AI’s ability to generate large-scale 3D landscapes that do not have preset limitations or boundaries. The generated 3D scenes can extend indefinitely, displaying diversity and variation throughout.
What’s the role of the semantic field in SceneDreamer?
The semantic field in SceneDreamer provides detailed scene semantics. It brings essential information about the landscape’s details such as the type, shape, and distribution of various elements in the generated 3D landscapes.
What technologies underpin SceneDreamer’s functionality?
SceneDreamer utilizes several distinct technologies for its functionality. Firstly, it uses simplex noise to create a bird’s-eye-view scene representation. Further, it employs a generative neural hash grid to encode generalizable features across different scenes. Lastly, it leverages a neural volumetric renderer, trained on 2D images via adversarial training, to produce photorealistic renderings.
