Neural Surface Reconstruction

Neural surface reconstruction aims to create accurate 3D surface models from multiple 2D images or other sensor data, focusing on overcoming challenges like sparse views, noisy data, and complex surface properties (e.g., specular reflections). Current research heavily utilizes neural implicit representations, often based on signed distance functions (SDFs) and differentiable volume rendering, with architectures like NeuS and its variants being prominent. These advancements are significantly impacting fields like virtual and augmented reality, scientific visualization, and medical imaging by enabling high-fidelity 3D model creation from readily available image data.