A computer-implemented method of unsupervised neural network training for learning of local image descriptors, the method comprising: obtaining a first pair of images depicting a same scene, the pair of images including a first image with a first pixel grid and a second image with a second pixel grid, wherein the first pixel grid differs from the second pixel grid; applying a neural network having an initial set of parameters to the first image and the second image to generate a feature map F 1 for the first image and a feature map F 2 for the second image, F 1 comprising a first grid of local image descriptors and F 2 comprising a second grid of local image descriptors, wherein each local image descriptor in the first grid of local image descriptors corresponds to a respective pixel within the first pixel grid and each local image descriptor in the second grid of local image descriptors corresponds to a respective pixel within the second pixel grid; determining an initial correlation volume C 0 based on F 1 and F 2, wherein C 0 comprises local correlations of pixels of the first pixel grid with pixels of the second pixel grid, wherein each local correlation between a pixel of the first pixel grid and a pixel of the second pixel grid is determined based on the local image descriptors corresponding to the correlated pixels; determining a high-level correlation volume C L based on C 0 by iterative pyramid construction, wherein C L comprises aggregated high-level correlations between iteratively constructed high-level patches of the first and second pixel grids, each patch being a rectangular portion of the respective pixel grid; determining a uniqueness matching loss for F 1 and F 2 based on C L, the uniqueness matching loss providing a measure for a deviation of F 1 and F 2 from optimal feature maps, wherein the deviation from optimal feature maps is low if, based on the high-level correlations in C L, each high-level patch of the first and the second pixel grids has a unique match among the high-level patches of the respective other pixel grid; and training the neural network by minimizing a loss function based on the uniqueness matching loss to generate an optimized set of parameters, thereby generating a trained neural network adapted for determining optimal local image descriptors. obtaining a first pair of images depicting a same scene, the pair of images including a first image with a first pixel grid and a second image with a second pixel grid, wherein the first pixel grid differs from the second pixel grid; applying a neural network having an initial set of parameters to the first image and the second image to generate a feature map F 1 for the first image and a feature map F 2 for the second image, F 1 comprising a first grid of local image descriptors and F 2 comprising a second grid of local image descriptors, wherein each local image descriptor in the first grid of local image descriptors corresponds to a respective pixel within the first pixel grid and each local image descriptor in the second grid of local image descriptors corresponds to a respective pixel within the second pixel grid; determining an initial correlation volume C 0 based on F 1 and F 2, wherein C 0 comprises local correlations of pixels of the first pixel grid with pixels of the second pixel grid, wherein each local correlation between a pixel of the first pixel grid and a pixel of the second pixel grid is determined based on the local image descriptors corresponding to the correlated pixels; determining a high-level correlation volume C L based on C 0 by iterative pyramid construction, wherein C L comprises aggregated high-level correlations between iteratively constructed high-level patches of the first and second pixel grids, each patch being a rectangular portion of the respective pixel grid; determining a uniqueness matching loss for F 1 and F 2 based on C L, the uniqueness matching loss providing a measure for a deviation of F 1 and F 2 from optimal feature maps, wherein the deviation from optimal feature maps is low if, based on the high-level correlations in C L, each high-level patch of the first and the second pixel grids has a unique match among the high-level patches of the respective other pixel grid; and training the neural network by minimizing a loss function based on the uniqueness matching loss to generate an optimized set of parameters, thereby generating a trained neural network adapted for determining optimal local image descriptors.