Image Based 3D Reconstruction

Image-based 3D reconstruction aims to create detailed three-dimensional models from multiple two-dimensional images, a crucial task in fields like robotics and augmented reality. Current research heavily emphasizes learning-based methods, employing architectures such as Neural Radiance Fields (NeRFs), Gaussian splatting, and multi-view stereo (MVS) algorithms, often incorporating techniques to improve efficiency by selecting informative views or rays. These advancements focus on enhancing accuracy, reducing computational cost, and handling complex scenarios like dynamic scenes and challenging lighting conditions. The resulting improvements in 3D model generation have significant implications for various applications, including autonomous navigation, virtual and augmented reality experiences, and cultural heritage preservation.