  Motivated by the success of encoding multi-scale contextual information for
image analysis, we propose our PointAtrousGraph (PAG) - a deep
permutation-invariant hierarchical encoder-decoder for efficiently exploiting
multi-scale edge features in point clouds. Our PAG is constructed by several
novel modules, such as Point Atrous Convolution (PAC), Edge-preserved Pooling
(EP) and Edge-preserved Unpooling (EU). Similar with atrous convolution, our
PAC can effectively enlarge receptive fields of filters and thus densely learn
multi-scale point features. Following the idea of non-overlapping max-pooling
operations, we propose our EP to preserve critical edge features during
subsampling. Correspondingly, our EU modules gradually recover spatial
information for edge features. In addition, we introduce chained skip
subsampling/upsampling modules that directly propagate edge features to the
final stage. Particularly, our proposed auxiliary loss functions can further
improve our performance. Experimental results show that our PAG outperform
previous state-of-the-art methods on various 3D semantic perception
applications.
