Chen, CanFragonara, Luca ZanottiTsourdos, Antonios2020-08-122020-08-122020-04-01Chen C, Zanotti Fragonara L and Tsourdos A. (2020) Go wider: an efficient neural network for point cloud analysis via group convolutions. Applied Sciences, Volume 10, Issue 7, 2020, Article number 23912076-3417https://doi.org/10.3390/app10072391https://dspace.lib.cranfield.ac.uk/handle/1826/15678In order to achieve a better performance for point cloud analysis, many researchers apply deep neural networks using stacked Multi-Layer-Perceptron (MLP) convolutions over an irregular point cloud. However, applying these dense MLP convolutions over a large amount of points (e.g., autonomous driving application) leads to limitations due to the computation and memory capabilities. To achieve higher performances but decrease the computational complexity, we propose a deep-wide neural network, named ShufflePointNet, which can exploit fine-grained local features, but also reduce redundancies using group convolution and channel shuffle operation. Unlike conventional operations that directly apply MLPs on the high-dimensional features of a point cloud, our model goes “wider” by splitting features into groups with smaller depth in advance, having the respective MLP computations applied only to a single group, which can significantly reduce complexity and computation. At the same time, we allow communication between groups by shuffling the feature channel to capture fine-grained features. We further discuss the multi-branch method for wider neural networks being also beneficial to feature extraction for point clouds. We present extensive experiments for shape classification tasks on a ModelNet40 dataset and semantic segmentation task on large scale datasets ShapeNet part, S3DIS and KITTI. Finally, we carry out an ablation study and compare our model to other state-of-the-art algorithms to show its efficiency in terms of complexity and accuracyenAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/neural networkfeature shuffleshape classificationsemantic segmentationpoint cloudArticle