  Learning discriminative feature directly on point clouds is still challenging
in the understanding of 3D shapes. Recent methods usually partition point
clouds into local region sets, and then extract the local region features with
fixed-size CNN or MLP, and finally aggregate all individual local features into
a global feature using simple max pooling. However, due to the irregularity and
sparsity in sampled point clouds, it is hard to encode the fine-grained
geometry of local regions and their spatial relationships when only using the
fixed-size filters and individual local feature integration, which limit the
ability to learn discriminative features. To address this issue, we present a
novel Local-Region-Context Network (LRC-Net), to learn discriminative features
on point clouds by encoding the fine-grained contexts inside and among local
regions simultaneously. LRC-Net consists of two main modules. The first module,
named intra-region context encoding, is designed for capturing the geometric
correlation inside each local region by novel variable-size convolution filter.
The second module, named inter-region context encoding, is proposed for
integrating the spatial relationships among local regions based on spatial
similarity measures. Experimental results show that LRC-Net is competitive with
state-of-the-art methods in shape classification and shape segmentation
applications.
