Gaussian Process Implicit Surface

Gaussian Process Implicit Surfaces (GPIS) are probabilistic models used to represent 3D shapes and surfaces from incomplete or noisy data, aiming for accurate reconstruction and uncertainty quantification. Current research focuses on integrating GPIS with other techniques, such as Gaussian Mixture Models (GMMs) for improved efficiency and handling of complex shapes, and applying them in robotics for tasks like manipulation and navigation, often incorporating visual and tactile sensor data. This approach offers significant advantages in applications requiring robust 3D modeling in uncertain environments, improving the reliability of tasks ranging from autonomous driving to medical imaging.