  Representation learning from 3D point clouds is challenging due to their
inherent nature of permutation invariance and irregular distribution in space.
Existing deep learning methods follow a hierarchical feature extraction
paradigm in which high-level abstract features are derived from low-level
features. However, they fail to exploit different granularity of information
due to the limited interaction between these features. To this end, we propose
Multi-Abstraction Refinement Network (MARNet) that ensures an effective
exchange of information between multi-level features to gain local and global
contextual cues while effectively preserving them till the final layer. We
empirically show the effectiveness of MARNet in terms of state-of-the-art
results on two challenging tasks: Shape classification and Coarse-to-fine
grained semantic segmentation. MARNet significantly improves the classification
performance by 2% over the baseline and outperforms the state-of-the-art
methods on semantic segmentation task.
