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.

Triplanar convolution with shared 2D kernels for 3D classification and shape retrieval Abstract   Increasing the depth of Convolutional Neural Networks (CNNs) has been recognized to provide better generalization performance. However, in the case of 3D CNNs, stacking layers increases the number of learnable parameters linearly, making it more prone to learn redundant features. In this paper, we propose a novel 3D CNN structure that learns shared 2D triplanar features viewed from the three orthogonal planes, which we term S3PNet. Due to the reduced dimension of the convolutions, the proposed S3PNet is able to learn 3D representations with substantially fewer learnable parameters. Experimental evaluations show that the combination of 2D representations on the different orthogonal views learned through the S3PNet is sufficient and effective for 3D representation, with the results outperforming current methods based on fully 3D CNNs. We support this with extensive evaluations on widely used 3D data sources in computer vision: CAD models, LiDAR point clouds, RGBD images, and 3D Computed Tomography scans. Experiments further demonstrate that S3PNet has better generalization capability for smaller training sets, and learns more of kernels with less redundancy compared to kernels learned from 3D CNNs.