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.

利用深度学习方法从少量图像重建和去噪点云，通过使用基于图像的深度学习模型所提取的先验知识来改善约束条件下的重建，并通过引入语义一致性监督来规范化可微渲染器的训练。同时，提出了一种细调稳定扩散算法用于去噪点云的渲染图像，并论证了这些学习的滤波器如何能够去除无需 3D 监督的点云噪声，并与 DSS 和 PointRadiance 方法进行了对比，实现了在 Sketchfab 测试集和 SCUT 数据集上更高质量的 3D 重建。