Geometry Prior

Geometry priors are pre-existing knowledge about the shape and structure of objects or scenes, used to improve the accuracy and efficiency of 3D reconstruction and related tasks. Current research focuses on integrating these priors into neural network architectures, such as NeRFs and other implicit surface representations, often leveraging techniques like Gaussian splatting or deformable template fields to handle variations in shape and pose. This approach enhances performance in applications like novel view synthesis, visual odometry, and scene understanding, particularly in scenarios with limited data or noisy inputs, leading to more robust and efficient 3D modeling.