View Dependent Normal Compensation

View-dependent normal compensation addresses inconsistencies in 3D scene reconstruction and other visual tasks arising from variations in viewpoint. Current research focuses on incorporating view-dependent biases into neural implicit representations, such as neural signed distance fields (SDF) and Gaussian fields, often employing neural networks to learn and correct these biases for improved accuracy and efficiency. These techniques are improving the quality of 3D models, high-resolution projector compensation, and low-light image enhancement, demonstrating the practical impact of addressing view-dependent artifacts in various computer vision applications. Furthermore, the concept extends beyond visual data, with applications in fair ranking algorithms aiming to compensate for biases in decision-making systems.