PointAGCN: Adaptive Spectral Graph CNN for Point Cloud Feature Learning Feature learning on unstructured 3D point clouds using deep networks is gaining attention due to its wide applications in robotics, selfdriving and so on. Among existing methods, PointNet has achieved promising results by directly working with point cloud data. However, it does not take full advantages of neighboring points that contain finegrained structural information which is helpful to better semantic learning. In the paper, we propose an adaptive spectral graph convolutional network for 3D point cloud feature processing, named PointAGCN. Our model use localized spectral graph convolution to capture local geometric features instead of designing of a powerful localized filter manually. The topology of the graph in each layer can be dynamically updated in each layer, which can bring more flexibility and generality. A novel graph pooling operation is carried out on the knearest neighbor graph, which aggregates features at graph nodes. Through extensive experiments on various datasets, the results show that the proposed approach achieves competitive performance on standard benchmarks.

EPN: EdgeAware PointNet for Object Recognition from MultiView 2.5D Point Clouds Performance of current 3D point based detectors is limited by the number of points they can process, consequently limiting their accuracy. In this paper we propose a novel architecture coined as EdgeAware PointNet, that incorporates geometric shape priors as binary maps, integrated in parallel with the PointNet framework, through convolutional neural networks (CNNs). The proposed architecture takes individual object instances as input and learns the task of object recognition for 3D shapes. To train the network, we present a dataset of 31k 2.5D synthetic point clouds rendered from ModelNet40. Through 2.5D representation, the network learns object recognition despite occlusion that enables improved performance on objects from real world, while 2D binary maps enable feature learning that is independent of number of points in the point cloud. Comprehensive experimentation shows that the proposed network is able to improve performance by 2.5% on ModelNet40 and 2.6% on ModelNet10 datasets, as compared to the baseline PointNet. We also show improved performance as compared to stateoftheart methods, on a real world RGBD dataset where our network improves results by 8%. Our code and dataset is publicly available at github.comMerium88EdgeAwarePointNet.