NeRF-VAE: A Geometry Aware 3D Scene Generative Model

We propose <PRE_TAG>NeRF-VAE</POST_TAG>, a 3D scene generative model that incorporates geometric structure via NeRF and differentiable volume rendering. In contrast to NeRF, our model takes into account shared structure across scenes, and is able to infer the structure of a novel scene -- without the need to re-train -- using amortized inference. <PRE_TAG>NeRF-VAE</POST_TAG>'s explicit 3D rendering process further contrasts previous generative models with convolution-based rendering which lacks geometric structure. Our model is a VAE that learns a distribution over radiance fields by conditioning them on a latent scene representation. We show that, once trained, <PRE_TAG>NeRF-VAE</POST_TAG> is able to infer and render geometrically-consistent scenes from previously unseen 3D environments using very few input images. We further demonstrate that <PRE_TAG>NeRF-VAE</POST_TAG> generalizes well to out-of-distribution cameras, while convolutional models do not. Finally, we introduce and study an attention-based conditioning mechanism of <PRE_TAG>NeRF-VAE</POST_TAG>'s decoder, which improves model performance.