Structural Relation Modeling of 3D Point Clouds.

In this paper, we propose an effective plug-and-play module called structural relation network (SRN) to model structural dependencies in 3D point clouds for feature representation. Existing network architectures such as PointNet++ and RS-CNN capture local structures individually and ignore the inner interactions between different sub-clouds. Motivated by the fact that structural relation modeling plays critical roles for humans to understand 3D objects, our SRN exploits local information by modeling structural relations in 3D spaces. For a given sub-cloud of point sets, SRN firstly extracts its geometrical and locational relations with the other sub-clouds and maps them into the embedding space, then aggregates both relational features with the other sub-clouds. As the variation of semantics embedded in different sub-clouds is ignored by SRN, we further extend SRN to enable dynamic message passing between different sub-clouds. We propose a graph-based structural relation network (GSRN) where sub-clouds and their pairwise relations are modeled as nodes and edges respectively, so that the node features are updated by the messages along the edges. Since the node features might not be well preserved when acquiring the global representation, we propose a Combined Entropy Readout (CER) function to adaptively aggregate them into the holistic representation, so that GSRN simultaneously models the local-local and local-global region-wise interaction. The proposed SRN and GSRN modules are simple, interpretable, and do not require any additional supervision signals, which can be easily equipped with the existing networks. Experimental results on the benchmark datasets (ScanObjectNN, ModelNet40, ShapeNet Part, S3DIS, ScanNet and SUN-RGBD) indicate promising boosts on the tasks of 3D point cloud classification, segmentation and object detection.