Geometry Reconstruction

Geometry reconstruction aims to create accurate 3D models from various input data, such as images, videos, or point clouds, focusing on improving both the speed and fidelity of reconstruction. Current research emphasizes novel neural network architectures, including those based on Gaussian splatting, NeRFs (Neural Radiance Fields), and transformers, often incorporating techniques like multi-view fusion, active illumination, and implicit surface representations to enhance accuracy and handle challenging scenarios like occlusions and sparse data. These advancements have significant implications for diverse fields, including robotics, virtual and augmented reality, and medical imaging, by enabling more realistic and efficient 3D modeling for various applications.