Taxonomy Driven Learning of Semantic Hierarchy of Classes

We present a new paradigm for rigid alignment between point clouds based on learnable weighted consensus named Deep Weighted Consensus (DWC). Current models, learnable or axiomatic, work well for constrained orientations and limited noise levels, usually by an end-to-end learner or an iterative scheme. However, real-world tasks require dealing with large rotations and outliers, and all known models fail to deliver. Here we present a different direction. We claim that we can align point clouds out of sampled matched points according to confidence level derived from a dense, soft alignment map. The pipeline is differentiable and converges under large rotations in the full spectrum of the rotation group, even with high noise levels.