Real Time 3D Reconstruction

Real-time 3D reconstruction aims to create three-dimensional models of environments or objects instantaneously using sensor data, primarily from cameras. Current research focuses on improving efficiency and accuracy through novel algorithms like Gaussian splatting and neural radiance fields (NeRFs), often incorporating deep learning for tasks such as pose estimation, depth prediction, and surface representation. These advancements are driving progress in applications such as robotics, augmented reality, and mobile mapping, enabling more dynamic and responsive interactions with the 3D world. The development of robust and computationally efficient methods is crucial for expanding the practical applications of real-time 3D reconstruction.