
Augmentation-free unsupervised approach for point clouds
Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.
The European AI, Data and Robotics stakeholders operate from diverse historic backgrounds. To tap into the potentials of the diverse knowledge and resources generated in the AI, Data, and Robotics ecosystem, an all-inclusive collaboration approach between these stakeholders is necessary to make the ADR resources available and accessible to all.
For this, the ADR Awareness Centre is developed as an open repository of ADR educational resources and materials to enable collaboration and alignment among all projects within the Partnership, relevant external projects, and the public. The Awareness Centre will collect, validate, and publish educational resources and materials related to AI, Data, and Robotics.
Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.