Semantic segmentation in 3D meshes is the classification of its constituent element (s) into
specific classes or categories. Using the powerful feature extraction abilities of deep neural …

The acquisition of large-scale 3D urban scene by photogrammetry and remote sensing is
becoming faster and easier in recent years. As one of the important steps to help machines …

Recent developments in data acquisition technology allow us to collect 3D texture meshes
quickly. Those can help us understand and analyse the urban environment, and as a …

The semantic classification of point clouds is a fundamental part of three-dimensional urban
reconstruction. For datasets with high spatial resolution but significantly more noises, a …

Commercial buildings as well as residential houses represent core structures of any modern
day urban or semiurban areas. Consequently, 3-D models of urban buildings are of …

Interpreting 3D point cloud data of the interior and exterior of buildings is essential for
automated navigation, interaction and 3D reconstruction. However, the direct exploitation of …

Textured 3-D mesh is one of the final user products in photogrammetry and remote sensing.
However, research on the semantic segmentation of complex urban scenes represented by …

Analyzing and extracting geometric features from 3D data is a fundamental step in 3D scene
understanding. Recent works demonstrated that deep learning architectures can operate …

Semantic segmentation of 3D scenes is one of the most important tasks in the field of
computer vision and has attracted much attention. In this paper, we propose a novel …

We introduce a novel approach for the polygonization of Multi-view Stereo (MVS) meshes of
buildings, which results in compact and topologically valid models. The main characteristic …