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Exploring contextual information in the local region is important for shapeunderstanding and analysis. Existing studies often employ hand-crafted orexplicit ways to encode contextual information of local regions. However, it ishard to capture fine-grained contextual information in hand-crafted or explicitmanners, such as the correlation between different areas in a local region,which limits the discriminative ability of learned features. To resolve thisissue, we propose a novel deep learning model for 3D point clouds, namedPoint2Sequence, to learn 3D shape features by capturing fine-grained contextualinformation in a novel implicit way. Point2Sequence employs a novel sequencelearning model for point clouds to capture the correlations by aggregatingmulti-scale areas of each local region with attention. Specifically,Point2Sequence first learns the feature of each area scale in a local region.Then, it captures the correlation between area scales in the process ofaggregating all area scales using a recurrent neural network (RNN) basedencoder-decoder structure, where an attention mechanism is proposed tohighlight the importance of different area scales. Experimental results showthat Point2Sequence achieves state-of-the-art performance in shapeclassification and segmentation tasks.

Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network