Abstract: A computer-implemented method of machine-learning for learning a neural network that encodes a super-point of a 3D point cloud into a latent vector. The method including obtaining a dataset of super-points. Each super-point is a set of points of a 3D point cloud. The set of points represents at least a part of an object. The method further includes learning the neural network based on the dataset of super-points. The learning includes minimizing a loss. The loss penalizes a disparity between two super-points. This constitutes improved machine-learning for 3D object detection.