Efficient and accurate identification of visual overlapping image pairs is an ongoing
challenge for large-scale Structure from Motion (SfM). Recently, CNN-based methods have …

We present PolyGNN, a polyhedron-based graph neural network for 3D building
reconstruction from point clouds. PolyGNN learns to assemble primitives obtained by …

Digital terrain models (DTMs) are pivotal in remote sensing cartography and landscape
management requiring accurate surface representation and topological information …

The benefits of having digital twins of urban buildings are numerous. However, a major
difficulty encountered in their creation from airborne LiDAR point clouds is the effective …

Automatic 3D reconstruction from spaceborne charge-coupled device (CCD) optical imagery
is still a challenge as the rational functional model (RFM) based reconstruction pipeline …

A low-resolution digital surface model (DSM) features distinctive attributes impacted by
noise, sensor limitations and data acquisition conditions, which failed to be replicated using …

Dense depth information can be reconstructed from stereo images using conventional hand-
crafted as well as deep learning-based approaches. While deep-learning methods often …

Nowadays, DSM (Digital Surface Model) is one of most important products that has been
widely applied in digital city or smart city. Over the last decades, the common way is to follow …

In recent decades, the geospatial domain has benefitted from technological advances in
sensors, methodologies, and processing tools to expand capabilities in mapping …

In the training process of the implicit 3D reconstruction network, the choice of spatial query
points' sampling strategy affects the final performance of the model. Different works have …