DiffRF: Rendering-Guided 3D Radiance Field Diffusion

Figure 1 . Our method performs denoising of a probabilistic diffusion process applied to 3D radiance fields. Guided by 3D supervision and volumetric rendering, our model enables the unconditional synthesis of high-fidelity 3D assets (left). We further introduce the novel application of masked completion (right), i.e., the task of recovering shape and appearance from incomplete objects (highlighted in lightblue on the top right chair), solved by our model as conditional inference without task-specific training.