4 Dimensional Reconstruction

4D reconstruction aims to capture both the three-dimensional geometry and temporal evolution of dynamic scenes, enabling the creation of realistic digital representations of moving objects and environments. Current research focuses on developing efficient and robust algorithms, often employing neural networks such as Gaussian splatting and implicit neural representations, to reconstruct 4D data from various input modalities, including monocular and multi-view videos, point clouds, and even generated videos. These advancements are driving progress in fields like robotics, augmented/virtual reality, and medical imaging, where accurate and detailed 4D models are crucial for tasks ranging from object manipulation to surgical planning. The development of large-scale datasets and open-source tools is also facilitating broader adoption and further research in this rapidly evolving area.