Abstract: According to an embodiment of the disclosure, a method performed by an apparatus may include obtaining a plurality of images, each of the plurality of images comprising a view of a scene. The method may include computing respective scores for each of the plurality of images. The method may include estimating respective camera poses of each of the plurality of images. The method may include using the computed scores and the estimated camera poses to determine a new camera pose useable for generating a new image comprising a view of the scene and having a score greater than a first threshold score. The method may include generating the new image using the new camera pose.

Abstract: A computer-implemented federated learning method is disclosed. The method comprises: for each of a number, n, of clients: determining a diversity score of a dataset corresponding to that client for training a machine learning model, wherein the diversity score is a measure of dataset variability; aggregating, weighted by the respective diversity score, models corresponding to each of the clients; and sending the aggregated model to at least one receiving client.

Abstract: Broadly speaking, embodiments of the present techniques provide methods for performing shadow detection and/or removal in images. In particular, the present techniques provide a computer-implemented method for generating a synthetic training dataset for training a machine learning, ML, model using federated learning to perform shadow detection (and optionally removal), and methods for training the ML model using the generated training dataset. Advantageously, the method to generate a training dataset enables a diverse training dataset to be generated while maintaining user data privacy, where the diversity refers to the variety of scenes containing shadows.