Robots and other smart devices need efficient object-based scene
representations from their on-board vision systems to reason about contact,
physics and occlusion. Recognized precise object models will play an important
role alongside non-parametric reconstructions of unrecognized structures. We
present a system which can estimate the accurate poses of multiple known
objects in contact and occlusion from real-time, embodied multi-view vision.
Our approach makes 3D object pose proposals from single RGB-D views,
accumulates pose estimates and non-parametric occupancy information from
multiple views as the camera moves, and performs joint optimization to estimate
consistent, non-intersecting poses for multiple objects in contact.
We verify the accuracy and robustness of our approach experimentally on 2
object datasets: YCB-Video, and our own challenging Cluttered YCB-Video. We
demonstrate a real-time robotics application where a robot arm precisely and
orderly disassembles complicated piles of objects, using only on-board RGB-D
vision.

提出了一种基于多视角视野的实时视觉系统，可以从单个 RGB-D 视角提出三维物体姿态建议，并且根据多个视角的姿态估计和非参数占用信息积累这些姿态估计，从而对接触的多个已知对象进行一致的、不相交的姿态估计。应用于实时机器人应用程序中，只使用机载 RGB-D 视觉，机械臂可以精确而有序地拆卸复杂的物体堆放，展示了其准确性和鲁棒性。