  Traditional grid/neighbor-based static pooling has become a constraint for
point cloud geometry analysis. In this paper, we propose DAR-Net, a novel
network architecture that focuses on dynamic feature aggregation. The central
idea of DAR-Net is generating a self-adaptive pooling skeleton that considers
both scene complexity and local geometry features. Providing variable
semi-local receptive fields and weights, the skeleton serves as a bridge that
connect local convolutional feature extractors and a global recurrent feature
integrator. Experimental results on indoor scene datasets show advantages of
the proposed approach compared to state-of-the-art architectures that adopt
static pooling methods.
