MARNet: Multi-Abstraction Refinement Network for 3D Point Cloud Analysis
- URL: http://arxiv.org/abs/2011.00923v1
- Date: Mon, 2 Nov 2020 12:07:35 GMT
- Title: MARNet: Multi-Abstraction Refinement Network for 3D Point Cloud Analysis
- Authors: Rahul Chakwate, Arulkumar Subramaniam, Anurag Mittal
- Abstract summary: Existing deep learning methods fail to exploit different granularity of information due to limited interaction between features.
We propose Multi-Abstraction Refinement Network (MARNet) that ensures an effective exchange of information between multi-level features.
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.
- Score: 9.34612743192798
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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