  The analysis of 3D point clouds has diverse applications in robotics, vision
and graphics. Processing them presents specific challenges since they are
naturally sparse, can vary in spatial resolution and are typically unordered.
Graph-based networks to abstract features have emerged as a promising
alternative to convolutional neural networks for their analysis, but these can
be computationally heavy as well as memory inefficient. To address these
limitations we introduce a novel Multi-level Graph Convolution Neural (MLGCN)
model, which uses Graph Neural Networks (GNN) blocks to extract features from
3D point clouds at specific locality levels. Our approach employs precomputed
graph KNNs, where each KNN graph is shared between GCN blocks inside a GNN
block, making it both efficient and effective compared to present models. We
demonstrate the efficacy of our approach on point cloud based object
classification and part segmentation tasks on benchmark datasets, showing that
it produces comparable results to those of state-of-the-art models while
requiring up to a thousand times fewer floating-point operations (FLOPs) and
having significantly reduced storage requirements. Thus, our MLGCN model could
be particular relevant to point cloud based 3D shape analysis in industrial
applications when computing resources are scarce.
