Training a neural network to generate new observations of a scenery

A method for training a neural network that predicts one or more characteristic values of a scenery that relate to locations observable from a specified measurement location, comprising the following steps:

• providing a set of training examples, each training example including at least (i) a measurement location and (ii) a reference observation of the scenery that relates to the measurement location;
• for each training example, supplying the measurement location of the training example to the neural network such that the neural network outputs a prediction of the one or more characteristic values;
• translating the predicted one or more characteristic values, using a specified rendering model, into an ideal observation of the scenery that is to be expected under idealized conditions when observed from the measurement location;
• and
• translating the ideal observation, using a measurement model, into a real observation of the scenery that is to be expected under realistic conditions when observed from the measurement location, wherein the measurement model represents a process with which the reference observations in the training examples were obtained;
• comparing the real observation with the reference observation of the training example;
• evaluating, using a specified cost function, a deviation determined during the comparison;
• and
• optimizing one or more parameters that characterize behavior of the neural network with a goal of improving the evaluation by the cost function during further processing of the training examples.

A non-transitory machine-readable data carrier on which is stored a computer program including machine-readable instructions for training a neural network that predicts one or more characteristic values of a specified scenery that relate to locations observable from a specified measurement location, the instructions, when executed by one or more computers and/or compute instances, causing the one or more computers and/or compute instances to perform the following steps comprising:

• providing a set of training examples, each training example of the training examples containing at least combinations of measurement locations on the one hand and reference observations of the scenery relating to the measurement locations on the other hand;
• for each training example, supplying the corresponding measurement location to the neural network, so that the neural network provides a prediction for the one or more characteristic values;
• translating the predicted one or more characteristic values, using a specified rendering model, into an ideal observation of the scenery that is to be expected under idealized conditions starting from the corresponding measurement location;
• translating the ideal observation, using a measurement model, into a real observation of the scenery that is to be expected under realistic conditions starting from the corresponding measurement location, wherein the measurement model represents a process with which the reference observations in the training examples were obtained;
• comparing the real observation with the reference observations for the corresponding training example;
• evaluating a deviation determined during the comparison using a specified cost function;
• and
• optimizing parameters that characterize a behavior of the neural network with a goal of improving the evaluation by the cost function during further processing of the training examples.

One or more computers including a non-transitory machine-readable data carrier on which is stored a computer program including machine-readable instructions for training a neural network that predicts one or more characteristic values of a specified scenery that relate to locations observable from a specified measurement location, the instructions, when executed by one or more computers, causing the one or more computers to perform the following steps comprising:

• providing a set of training examples, each training example of the training examples containing at least combinations of measurement locations on the one hand and reference observations of the scenery relating to the measurement locations on the other hand;
• for each training example, supplying the corresponding measurement location to the neural network, so that the neural network provides a prediction for the one or more characteristic values;
• translating the predicted one or more characteristic values, using a specified rendering model, into an ideal observation of the scenery that is to be expected under idealized conditions starting from the corresponding measurement location;
• translating the ideal observation, using a measurement model, into a real observation of the scenery that is to be expected under realistic conditions starting from the corresponding measurement location, wherein the measurement model represents a process with which the reference observations in the training examples were obtained;
• comparing the real observation with the reference observations for the corresponding training example;
• evaluating a deviation determined during the comparison using a specified cost function;
• and
• optimizing parameters that characterize a behavior of the neural network with a goal of improving the evaluation by the cost function during further processing of the training examples.