Volumetric Reconstruction

Volumetric reconstruction aims to create three-dimensional models of objects or scenes from various input data, such as images, MRI scans, or X-ray projections. Current research emphasizes improving accuracy and efficiency through novel algorithms, including Gaussian splatting, neural radiance fields (NeRFs), and transformer-based approaches, often incorporating techniques like differentiable rendering and self-supervised learning. These advancements are driving progress in diverse fields, from medical imaging (improving MRI and CT reconstruction) to computer vision (creating high-fidelity 3D models from images) and beyond, enabling more accurate and efficient analysis and visualization of complex data.