# Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions
## 1 Introduction  
Point cloud processing is an increasingly important task for a wide range of applications such as: environment perception for autonomous driving [1; 2; 3; 4], virtual reconstruction [5], or augmented reality (AR) [6]. It belongs to a deep learning branch that is typically referred to as _geometric deep learning_[7]. However, it is still challenging to analyse and extract useful features of the underlying shape representation efficiently because of the disadvantages of having a large amount of points and an unstructured distribution. On the other hand, point cloud data can provide a very accurate geometric information of a three-dimensional object. Considering the remarkable success and advantages of Convolutional Neural Networks (CNNs), various authors firstly tried to apply them over standard grid structure voxelized from an unordered point cloud, as CNNs only work on a regular grid data structure [8; 9]. However, this intuitive way of applying deep learning to point clouds leads to high memory and computation requirements due to the natural sparse and irregular structure of the data. _PointNet_[10] treats point cloud directly as a set of unordered points, leveraging a Multi-Layer-Perceptron (MLP) network and a symmetric function (e.g., max pooling) to exploit global features and make unordered points invariant to permutations. The main drawback is that the local information is not used in the stacked MLP layers. In order to solvethis issue, _PointNet++_[11] proposes a hierarchical neural network joining _PointNet_ with a sampling and grouping layer to capture local representation within the data. Similarly, _DGCNN_[12] extracts local features by introducing an edge convolution operation on points and edges connecting each point and corresponding neighbours. [PERSON] et al. [13] proposes an attention-aware neural network to learn local features by highlighting different attention coefficient for neighbouring points. Another CNN-like approach is _PointCNN_[14], which manages to transform an unordered point cloud to a latent canonical order by using a \(\chi\)-convolutional operator. _RS-CNN_[15] and _ConvPoint_[16] attempt to learn irregular CNN-like filters to capture local features for point cloud. Our model applies feature shuffling operations on _PointNet++_[11] for less model computation but still with high accuracy performance. As can be seen in Figure 1, it performs the best trade-off in accuracy and model complexity. We notice that the modern neural networks proposed for geometric deep learning [10; 11; 12; 13] heavily relies on dense MLP used to build repeated block structures with a different number of filters. On the other hand, the elimination of redundancies within these dense MLP operations is rarely mentioned for point cloud analysis, although it is a relatively common topic in the computer vision domain [17; 18; 19; 20; 21]. Within the point cloud processing domain, many applications (e.g., autonomous driving) normally have to process large scale point clouds, which make model complexity increase significantly. Besides this, the MLP operation is not a spatial convolution, which limits the local feature extraction capabilities of the overall model. As a result, applying a large number of deep MLP convolutions easily leads to data overfitting. However, the MLP operation is efficient in dealing with unstructured data (e.g., point cloud, social networks) as regular CNNs are only suited to deal with data having a standard grid structure. On the other hand, these dense and deep \(1\times 1\) convolutions are not efficient in terms of computation and memory. Therefore, we draw attention to the use of sparse MLP  
Figure 1: Measurement of accuracy, parameters, floating point operations (FLOPs) and forward time. It shows that our proposed ShufflePointNet (magenta diamond marker) tends to stay in the bottom-right corner, which means that our model conjugates high accuracy with low model complexity.