ABSTRACT Three-dimensional (3D) reconstruction of a building can be facilitated by
correctly segmenting different feature points (eg in the form of boundary, fold edge, and …

We propose a deep learning-based reconstruction method for three-dimensional (3D)
cultural heritage objects, which are destroyed or disappear and thus are unavailable for 3D …

Existing approaches that extract buildings from point cloud data do not select the
appropriate neighbourhood for estimation of normals on individual points. However, the …

Classification of 3D scatter and unorganized point cloud (PC) is an ongoing hard problem
due to high redundancy, unbalanced sampling density, and large data structure of PC …

We propose a contextual label-smoothing method to improve the LiDAR classification
accuracy in a post-processing step. Under the framework of global graph-structured …

3D point cloud classification has wide applications in the field of scene understanding. Point
cloud classification based on points can more accurately segment the boundary region …

Semantic segmentation of urban areas using Light Detection and Ranging (LiDAR) point
cloud data is challenging due to the complexity, outliers, and heterogeneous nature of the …

ABSTRACT 3D scene analysis and classification by automatically assigning semantic labels
to 3D points has become a major concern in remote sensing, photogrammetry and computer …

Segmentation and visualization of three-dimensional digital cultural heritage are important
analytical tools for the intuitive understanding of content. In this paper, we propose a …

Recent advances in 3D scanning have enabled the digital recording of complex objects as
large-scale point clouds, which require clear visualization to convey their 3D shapes …