Point Cloud Denoising

Point cloud denoising aims to remove noise and outliers from 3D point cloud data, improving the accuracy and reliability of subsequent analyses and applications. Current research focuses on deep learning approaches, employing architectures like graph convolutional networks, transformers, and diffusion models, often incorporating geometric information and multi-scale features to achieve optimal denoising while preserving fine details. These advancements are crucial for various fields, including autonomous driving, 3D modeling, and robotics, where high-quality point cloud data is essential for accurate perception and decision-making. The development of efficient and robust denoising methods is driving progress in these areas by enabling more reliable and accurate processing of 3D data.