graph convolutional network

ASSANet: An Anisotropic Separable Set Abstraction forEfficient Point Cloud Representation Learning

Access to 3D point cloud representations has been widely facilitated by LiDAR sensors embedded in various mobile devices. This has led to an emerging needfor fast and accurate point cloud processing techniques. In this paper, we revisitand dive …

DeepGCNs: Making GCNs Go as Deep as CNNs

Convolutional Neural Networks (CNNs) have been very successful at solving a variety of computer vision tasks such as object classification and detection, semantic segmentation, activity understanding, to name just a few. One key enabling factor for …

PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks

Upsampling sparse, noisy, and non-uniform point clouds is a challenging task. In this paper, we propose 3 novel point upsampling modules: Multi-branch GCN, Clone GCN, and NodeShuffle. Our modules use Graph Convolutional Networks (GCNs) to better …

SGAS: Sequential Greedy Architecture Search

Architecture design has become a crucial component of successful deep learning. Recent progress in automatic neural architecture search (NAS) shows a lot of promise. However, discovered architectures often fail to generalize in the final evaluation. …