Surface Prior

Surface priors are increasingly used in 3D reconstruction and related fields to improve the accuracy and efficiency of algorithms working with sparse or incomplete data. Current research focuses on incorporating these priors into neural networks, often using implicit surface representations like signed distance functions (SDFs) or Gaussian functions as templates, to guide the reconstruction process from limited input views or point clouds. This approach addresses challenges in handling sparse data, leading to improved surface reconstruction quality in applications such as multi-view stereo and point cloud compression, ultimately enhancing the capabilities of 3D modeling and analysis techniques.