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Dynamic Edge Weights in Graph Neural Networks for 3D Object Detection
[article]
2020
arXiv
pre-print
A robust and accurate 3D detection system is an integral part of autonomous vehicles. Traditionally, a majority of 3D object detection algorithms focus on processing 3D point clouds using voxel grids or bird's eye view (BEV). Recent works, however, demonstrate the utilization of the graph neural network (GNN) as a promising approach to 3D object detection. In this work, we propose an attention based feature aggregation technique in GNN for detecting objects in LiDAR scan. We first employ a
arXiv:2009.08253v1
fatcat:hhhofo6bgjetvih6s2ndnk42xe