Implicit Reconstruction

Implicit reconstruction uses neural networks to represent 3D shapes as continuous functions, aiming to create accurate and detailed 3D models from various input data like images or point clouds. Current research focuses on improving the accuracy and efficiency of these methods, particularly by incorporating geometric and topological regularizations, leveraging multi-view and temporal information, and developing novel architectures like signed distance fields (SDFs) and normal deflection fields. This approach offers advantages in storage, querying, and handling complex shapes, with significant implications for applications such as 3D modeling, robotics, and virtual reality.