DiffRF: Rendering-Guided 3D Radiance Field Diffusion

• URL: http://arxiv.org/abs/2212.01206v2
• Date: Mon, 27 Mar 2023 14:51:07 GMT
• Title: DiffRF: Rendering-Guided 3D Radiance Field Diffusion
• Authors: Norman M\"uller, Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Bul\`o, Peter Kontschieder, Matthias Nie{\ss}ner
• Abstract summary: We introduce DiffRF, a novel approach for 3D radiance field synthesis based on denoising diffusion probabilistic models. In contrast to 2D-diffusion models, our model learns multi-view consistent priors, enabling free-view synthesis and accurate shape generation.
• Score: 18.20324411024166
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce DiffRF, a novel approach for 3D radiance field synthesis based on denoising diffusion probabilistic models. While existing diffusion-based methods operate on images, latent codes, or point cloud data, we are the first to directly generate volumetric radiance fields. To this end, we propose a 3D denoising model which directly operates on an explicit voxel grid representation. However, as radiance fields generated from a set of posed images can be ambiguous and contain artifacts, obtaining ground truth radiance field samples is non-trivial. We address this challenge by pairing the denoising formulation with a rendering loss, enabling our model to learn a deviated prior that favours good image quality instead of trying to replicate fitting errors like floating artifacts. In contrast to 2D-diffusion models, our model learns multi-view consistent priors, enabling free-view synthesis and accurate shape generation. Compared to 3D GANs, our diffusion-based approach naturally enables conditional generation such as masked completion or single-view 3D synthesis at inference time.

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