Automatic reconstruction of surfaces from satellite imagery is a hot topic in computer vision
and photogrammetry. State-of-the-art reconstruction methods typically produce 2.5 D …

Neural rendering methods for learning the appearance and geometry of 3D scenes have
gained tremendous popularity since 2020. In this field, NeRF or Neural Radiance Fields is …

Neural Radiance Fields (NeRFs) are an emerging approach to 3D reconstruction that use
neural networks to reconstruct scenes. However, its applications for multi-view satellite …

Radiance fields have been a major breakthrough in the field of inverse rendering novel view
synthesis and 3D modeling of complex scenes from multi-view image collections. Since their …

Abstract 3D modeling from satellite imagery is essential in areas of environmental science
urban planning agriculture and disaster response. However traditional 3D modeling …

3-D building models are one of the most intuitive and widely used forms for understanding
urban buildings. Generating 3-D building models based on a single off-nadir satellite image …

In urban scenes, there are man-made ground objects with complex structures and significant
height differences, which leads to challenges in generating large-scale true digital …

We introduce FusionRF, a novel neural rendering terrain reconstruction method from
optically unprocessed satellite imagery. While previous methods depend on external …

Three-dimensional reconstruction utilizing few-view satellite images can effectively reduce
the cost of resources. However, methods that rely on dense stereo matching suffer severe …

SAR images possess unique attributes that present challenges for both human observers
and vision AI models to interpret, owing to their electromagnetic characteristics. The …