MARNet: Multi-Abstraction Refinement Network for 3D Point Cloud Analysis

TL;DR

MARNet is a novel neural network architecture that enhances 3D point cloud analysis by facilitating multi-level feature interaction, leading to improved shape classification and semantic segmentation accuracy.

Contribution

It introduces a multi-abstraction refinement mechanism for better feature exchange across hierarchical levels in 3D point cloud processing.

Findings

Achieves 2% higher accuracy in shape classification.

Outperforms state-of-the-art in semantic segmentation.

Demonstrates effective multi-level feature interaction.

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.