"Enhancing Point-Cloud Completion using Text-to-Image Diffusion Models"

Researchers have developed SDS-Complete, a point cloud completion technique that leverages pre-trained text-to-image diffusion models to fill in missing parts. Traditional methods struggle with completing point clouds of objects not seen in the training set, but SDS-Complete combines prior knowledge from diffusion models with observed partial point clouds to generate accurate and realistic 3D shapes. The method utilizes the SDS loss and a Signed Distance Function (SDF) surface representation to ensure consistency with input points and preserve existing 3D content captured by different depth sensors.