Abstract: A method for training an ML model to generate a voxel-based 3D representation of an environment of a vehicle. The method includes: generating first image data, which represent the environment of the vehicle, based on at least one data source; extracting at least one image feature from the first image data using the trainable ML model; generating a voxel-based 3D representation for the environment using the trainable ML model by transforming the at least one image feature into a corresponding voxel feature, wherein each voxel feature contains occupancy information and color information of a 3D position of the voxel feature; rendering the generated 3D representation for the at least one voxel feature based on the color information and the occupancy information to generate second image data; comparing the first input image data with the generated second out image data, and adjusting at least one parameter of the ML model.

Abstract: A method for generating a generic 3D representation of a surroundings of a vehicle. The method includes: generating first image data, which represent the surroundings of the vehicle; extracting at least one image feature from the first image data using a trainable ML model; generating a voxel-based 3D representation for the surroundings of the vehicle using the trainable ML model in that the at least one image feature n the 2D domain is transformed into a corresponding voxel feature in a 3D domain, wherein the generated 3D representation for the at least one voxel feature stores information about the probability with which this 3D domain of this voxel s occupied; training the machine learning model to extract at least one image feature from at least one camera and to determine the occupancy of all 3D voxels.