Discriminative Recognition of Point Cloud Gesture Classes through OneShot Learning In this paper, we introduce a oneshot learning approach to extend learned point cloud gesture categories into recognizing newly introduced categories after training the original model. This approach is based on learning the discrimination between gesture classes of point cloud data, making it possible to recognize new classes without retraining the original deep neural network (DNN) model. We develop a temporal variant of the PointNet model referred to as Temporal PointNet or TPoinNet, which consumes a sequence of raw point clouds from a lowcost depth sensor and outputs the class of the sequence. We use a multitask strategy where the model learns the classification of a point cloud sequence and a Euclidean space discrimination between the classified sequence and another sequence of a different class. The new model is able to classify and map the point clouds sequence inputs into a Euclidean space where the distances between the gesture sequences correspond to the gesture similarities. We present results on a point cloud dataset and the MSR Action 3D dataset showing the discrimination of new gesture categories with a high precision.

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.