SESR: Self-Ensembling Sim-to-Real Instance Segmentation for Auto-Store Bin Picking
Biqi Yang,Xiaojie Gao,Kai Chen,Rui Cao,Yidan Feng,Xianzhi Li,Qi Dou,Chi-Wing Fu,Yun-Hui Liu,Pheng-Ann Heng,Biqi Yang,Xiaojie Gao,Kai Chen,Rui Cao,Yidan Feng,Xianzhi Li,Qi Dou,Chi-Wing Fu,Yun-Hui Liu,Pheng-Ann Heng
Instance segmentation is an important task for supporting robotic grasping in auto-store scenarios. Accurate segmentation usually relies on the quantity and quality of available annotated training data. However, it requires tremendous cost to obtain these labels. In this work, without requiring any human annotations on real data, our proposed self-ensembling sim-to-real network, namely SESR, is ab...
