STOPNet: Multiview-based 6-DoF Suction Detection for Transparent Objects
  on Production Lines
Yuxuan Kuang Qin Han Danshi Li Qiyu Dai Lian Ding Dong Sun Hanlin Zhao He Wang
In this work, we present STOPNet, a framework for 6-DoF object suction
detection on production lines, with a focus on but not limited to transparent
objects, which is an important and challenging problem in robotic systems and
modern industry. Current methods requiring depth input fail on transparent
objects due to depth cameras' deficiency in sensing their geometry, while we
proposed a novel framework to reconstruct the scene on the production line
depending only on RGB input, based on multiview stereo. Compared to existing
works, our method not only reconstructs the whole 3D scene in order to obtain
high-quality 6-DoF suction poses in real time but also generalizes to novel
environments, novel arrangements and novel objects, including challenging
transparent objects, both in simulation and the real world. Extensive
experiments in simulation and the real world show that our method significantly
surpasses the baselines and has better generalizability, which caters to
practical industrial needs.