A computer-implemented method for training a convolutional neural network, wherein the convolutional neural network is provided with a first set of consecutive layers, a second set of consecutive layers and a third set of consecutive layers, each layer being configured with one or more filters, wherein a fusion layer of the convolutional neural network is configured to fuse one or more image data features obtained by the first set of layers and one or more depth data features obtained by the second set of layers, wherein the convolutional neural network is trained to associate an image data item and a depth data item to an identification/classification of an object and corresponding 3D localization data for the identified/classified object, wherein the 3D localization data includes object reference point data, wherein the convolutional neural network is trained using training data, wherein each training data item includes image data and depth data, and 3D localization data, wherein at least a part of the training data items is synthetically generated, wherein a further training data item is generated from an image data item and corresponding depth data item of a training data item by - cropping or expanding randomly selected image parts of the image data from the training data item while maintaining the aspect ratio; - resizing the dimensions of the cropped/expanded image parts to the dimension of the image data item of the training data item by applying a zoom factor which depends on the dimension of the image data item of the training data item and the dimension of the cropped/expanded image parts; and - adapting the depth data item and the associated 3D localization data of the identified/classified object depending on the zoom factor to obtain the further training data item. wherein the convolutional neural network is provided with a first set of consecutive layers, a second set of consecutive layers and a third set of consecutive layers, each layer being configured with one or more filters, wherein a fusion layer of the convolutional neural network is configured to fuse one or more image data features obtained by the first set of layers and one or more depth data features obtained by the second set of layers, wherein the convolutional neural network is trained to associate an image data item and a depth data item to an identification/classification of an object and corresponding 3D localization data for the identified/classified object, wherein the 3D localization data includes object reference point data, wherein the convolutional neural network is trained using training data, wherein each training data item includes image data and depth data, and 3D localization data, wherein at least a part of the training data items is synthetically generated, wherein a further training data item is generated from an image data item and corresponding depth data item of a training data item by - cropping or expanding randomly selected image parts of the image data from the training data item while maintaining the aspect ratio; - resizing the dimensions of the cropped/expanded image parts to the dimension of the image data item of the training data item by applying a zoom factor which depends on the dimension of the image data item of the training data item and the dimension of the cropped/expanded image parts; and - adapting the depth data item and the associated 3D localization data of the identified/classified object depending on the zoom factor to obtain the further training data item. - cropping or expanding randomly selected image parts of the image data from the training data item while maintaining the aspect ratio; - resizing the dimensions of the cropped/expanded image parts to the dimension of the image data item of the training data item by applying a zoom factor which depends on the dimension of the image data item of the training data item and the dimension of the cropped/expanded image parts; and - adapting the depth data item and the associated 3D localization data of the identified/classified object depending on the zoom factor to obtain the further training data item.