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The performance of existing point cloud-based 3D object detection methods heavily relies on large-scale highquality 3D annotations. However, such annotations are often tedious and expensive to collect. Semi-supervised learning is a good alternative to mitigate the data annotation issue, but has remained largely unexplored in 3D object detection. Inspired by the recent success of self-ensembling technique in semi-supervised image classification task, we propose SESS, a self-ensemblingdoi:10.1109/cvpr42600.2020.01109 dblp:conf/cvpr/ZhaoCL20 fatcat:ljq5yrkb5ngt3l4ogxyijw4bce