Machine-learning for 3d object detection

A computer-implemented method of applying a neural network leaning according to machine-learning for learning a neural network configured to encode a super-point of a 3D point cloud into a latent vector, the method of machine-learning including obtaining a dataset of super-points, each super-point being a set of points of a 3D point cloud, the set of points representing at least a part of an object, and learning the neural network based on the dataset of super-points, the learning comprising minimizing a loss penalizing a disparity between two super-points, the method comprising:

• obtaining one or more first super-points of a first 3D point cloud representing a 3D scene;
• and
• obtaining one or more second super-points of a second 3D point cloud representing a 3D object;
• encoding, by applying the neural network, the one or more first super-points each into a respective first latent vector and the one or more second super-points each into a respective second latent vector;
• and
• determining a similarity between each first super-point of one or more first super-points and each second super-point of one or more second super-points, by, for said each first super-point and said each second super-point, computing a similarity measure between the respective first latent vector encoding the first super-point and the respective second latent vector encoding the second super-point.

A device comprising:

• a processor;
• and
• a computer readable data storage medium having recorded thereon
• a computer program comprising instructions
• for performing machine-learning for learning a neural network configured for encoding a super-point of a 3D point cloud into a latent vector by the processor being configured to obtain a dataset of super-points, each super-point being a set of points of a 3D point cloud, the set of points representing at least a part of an object, and learn the neural network based on the dataset of super-points, the learning comprising minimizing a loss penalizing a disparity between two super-points, and/or
• for applying a neural network learnable according to the machine-learning by the processor being configured to:
• obtain one or more first super-points of a first 3D point cloud representing a 3D scene, and
• obtain one or more second super-points of a second 3D point cloud representing a 3D object,
• encode, by applying the neural network, the one or more first super-points each into a respective first latent vector and the one or more second super-points each into a respective second latent vector;
• and
• determine a similarity between each first super-point of one or more first super-points and each second super-point of one or more second super-points, by, for said each first super-point and said each second super-point, computing a similarity measure between the respective first latent vector encoding the first super-point and the respective second latent vector encoding the second super-point, and/or
• a neural network learnable according to the machine-learning.