Object Point Cloud Classification via Poly-Convolutional Architecture Search

Existing point cloud classifiers concern on handling irregular data structures to discover a global and discriminative configuration of local geometries. These classification methods design a number of effective permutation-invariant feature encoding kernels, but still suffer from the intrinsic challenge of large geometric feature variations caused by inconsistent point distributions along object surface. In this paper, point cloud classification can be addressed via deep graph representation learning on aggregating multiple convolutional feature kernels (namely, a poly convolutional operation) anchored on each point with its local neighbours. Inspired by recent success of neural architecture search, we introduce a novel concept of poly-convolutional architecture search (PolyConv search in short) to model local geometric patterns in a more flexible manner. To this end, the Monte Carlo Tree Search (MCTS) method is adopted, which can be formulated into a Markov Decision Process problem to cast decisions for dependently selecting layer-wise aggregation kernels. Experiments on the popular ModelNet40 benchmark have verified that superior performance can be achieved by constructing networks via the MCTS method, with aggregation kernels in our PolyConv search space.