Normal Map

Normal maps are 2D representations of 3D surface orientation, crucial for various applications including 3D reconstruction, computer graphics, and robotics. Current research focuses on improving the accuracy and robustness of normal map estimation from various input sources like images and point clouds, employing techniques such as diffusion models, generative adversarial networks (GANs), and neural networks with sophisticated sampling and loss functions. These advancements enable more realistic 3D modeling, improved object recognition in robotics, and enhanced performance capture in animation and virtual reality. The development of efficient and accurate normal map generation and inpainting methods is driving progress in several related fields.