BladeNeRF: Exploiting camera constraints for NeRF in repetitive texture-less 3D reconstruction

Neural Radiance Fields (NeRFs) have demonstrated remarkable capabilities in photo-realistic 3D reconstruction. NeRFs often take as input posed images where the camera poses come from either off-the-shelf S\textit{f}M or online optimization together with NeRFs. However, we find that both strategies yield suboptimal results in recovering camera...

A trainable activation scheme for applying Neural Radiance Fields in novel view synthesis tasks

3D scene reconstruction is a common computer vision task with many applications. The synthesized virtual environments are beneficial for many downstream applications such as 3D modeling, building inspection, virtual reality, etc. As conventional scene reconstruction methods often require expensive data collections and prior information on the...

Indoor 3D Reconstruction from a Single Image

3D indoor reconstruction has been an important research area in the field of computer vision and photogrammetry. While the initial techniques developed for this purpose use sensor devices and multiple images for data acquisition and extracting 3D information and representation of the scene, with the advent of deep learning techniques, there has...