Adapting Point Transformer V3 for Point Cloud Denoising

A point cloud is a collection of millions of points scanned on an object's surface, and denoising these points is an essential process for the reconstruction of accurate 3D models, with important applications in autonomous driving, robotic navigation, and medical imaging. Point Transformer V3 (PTv3) is a transformer-based model designed for point cloud processing, but it was not originally developed for denoising tasks. In this project, we adapt PTv3 specifically for point cloud denoising to improve its ability to detect and remove noise from 3D data. We further improve performance by optimizing the implementation for modern versions of PyTorch, CUDA, and GPU hardware. These modifications increase both computational efficiency and denoising accuracy, enabling faster and more precise large-scale 3D data processing.