Surface Estimation

Surface estimation aims to reconstruct 3D surfaces from incomplete or noisy data, crucial for applications ranging from 3D modeling and medical imaging to augmented reality. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs) – sometimes in hybrid 2D-3D architectures – to achieve accurate and efficient surface reconstruction, often incorporating curvature information or signed distance functions for improved detail and robustness. These advancements enable more precise 3D models from various data sources, improving the accuracy and efficiency of numerous applications across diverse scientific fields and industries.