Abstract Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene
representation, offering high-fidelity renderings and reconstructions from a set of sparse and …

In recent years, Neural Radiance Fields (NeRF) has made remarkable progress in the field
of computer vision and graphics, providing strong technical support for solving key tasks …

Abstract Neural Radiance Field (NeRF) is a promising deep learning technique based on
neural rendering for three-dimensional (3D) reconstruction. This technique has overcome …

End-to-end autonomous driving with vision-only is not only more cost-effective compared to
LiDAR-vision fusion but also more reliable than traditional methods. To achieve a …

3D reconstruction is a long-standing research topic in the photogrammetric and computer
vision communities; although a plethora of open-source and commercial solutions for 3D …

As the power grid is an indispensable foundation of modern society, creating a digital twin of
the grid is of great importance. Pylons serve as components in the transmission corridor, and …

Three-dimensional (3D) reconstruction of trees has always been a key task in precision
forestry management and research. Due to the complex branch morphological structure of …

Neural radiance fields (NeRF) offer the potential to benefit 3D reconstruction tasks, including
aerial photogrammetry. However, the scalability and accuracy of the inferred geometry are …

Three-dimensional dense reconstruction involves extracting the full shape and texture
details of three-dimensional objects from two-dimensional images. Although 3D …

We evaluate different Neural Radiance Field (NeRF) techniques for the 3D reconstruction of
plants in varied environments, from indoor settings to outdoor fields. Traditional methods …