  Existing deep learning algorithms for point cloud analysis mainly concern
discovering semantic patterns from global configuration of local geometries in
a supervised learning manner. However, very few explore geometric properties
revealing local surface manifolds embedded in 3D Euclidean space to
discriminate semantic classes or object parts as additional supervision
signals. This paper is the first attempt to propose a unique multi-task
geometric learning network to improve semantic analysis by auxiliary geometric
learning with local shape properties, which can be either generated via
physical computation from point clouds themselves as self-supervision signals
or provided as privileged information. Owing to explicitly encoding local shape
manifolds in favor of semantic analysis, the proposed geometric self-supervised
and privileged learning algorithms can achieve superior performance to their
backbone baselines and other state-of-the-art methods, which are verified in
the experiments on the popular benchmarks.
