A New Approach to Point Cloud Data Generation and Object Completion Using Denoising Diffusion Probabilistic Models and PointNet++

We suggested a novel approach to address point cloud data processing challenges by applying denoising diffusion probabilistic models (DDPMs). Our method provides a comprehensive solution for point cloud data generation and object manipulation by leveraging the capabilities of PointNet++ and diffusion models to generate high-quality point coordinates. Firstly, we implemented a PointNet++ aimed at point cloud data that learns the underlying probability distribution of point representations and extracts meaningful features. Then, the model produces realistic spatial distributions using the denoising diffusion process. To improve feature extraction, we made some changes to the PointNet++ framework, which increased its efficiency in object completion tasks. The diffusion model uses these extracted features to generate and restore corrupted objects, and during sampling, we inject a target segment with noise into the reverse diffusion to produce a guided object with a specific segment