The relevance of point cloud data for capturing 3D environmental data in various scenarios, such as robotics, computer vision, autonomous driving, building and city digital twin models, is increasing rapidly. Despite its ability to accurately describe the geometry or shape of surface elements, it is still unstructured data, meaning it is only a group of points represented in 3D space with x, y, and z coordinates, as well as other features, such as color (R, G, B). Therefore, it is essential to provide meaning and structure to this data by segmenting or classifying the points into groups with specific semantic labels.
The relevance of point cloud data for capturing 3D environmental data in various scenarios, such as robotics, computer vision, autonomous driving, building and city digital twin models, is increasing rapidly. Despite its ability to accurately describe the geometry or shape of surface elements, it is still unstructured data, meaning it is only a group of points represented in 3D space with x, y, and z coordinates, as well as other features, such as color (R, G, B). Therefore, it is essential to provide meaning and structure to this data by segmenting or classifying the points into groups with specific semantic labels.
