  Existing deep learning methods for the reconstruction and denoising of point
clouds rely on small datasets of 3D shapes. We circumvent the problem by
leveraging deep learning methods trained on billions of images. We propose a
method to reconstruct point clouds from few images and to denoise point clouds
from their rendering by exploiting prior knowledge distilled from image-based
deep learning models. To improve reconstruction in constraint settings, we
regularize the training of a differentiable renderer with hybrid surface and
appearance by introducing semantic consistency supervision. In addition, we
propose a pipeline to finetune Stable Diffusion to denoise renderings of noisy
point clouds and we demonstrate how these learned filters can be used to remove
point cloud noise coming without 3D supervision. We compare our method with DSS
and PointRadiance and achieved higher quality 3D reconstruction on the
Sketchfab Testset and SCUT Dataset.
