论文标题
MARNET：用于3D点云分析的多重改进网络
MARNet: Multi-Abstraction Refinement Network for 3D Point Cloud Analysis
论文作者
论文摘要
从3D点云中进行的表示，由于其置换不变性和空间不规则分布的固有性质，因此具有挑战性。现有的深度学习方法遵循分层特征提取范式，其中高级抽象特征来自低级特征。但是，由于这些特征之间的相互作用有限，他们无法利用不同的信息粒度。为此，我们提出了多种精制网络（MARNET），以确保在多层次功能之间有效地交换信息以获得本地和全球上下文提示，同时有效地保留它们直到最后一层。我们从经验上显示了Marnet在最先进的结果方面对两个具有挑战性的任务的有效性：形状分类和粗到细粒度的语义细分。 Marnet在基线上将分类性能显着提高了2％，并优于语义细分任务的最新方法。
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.
