Real-time Simultaneous Multi-Object 3D Shape Reconstruction, 6DoF Pose
  Estimation and Dense Grasp Prediction
Shubham Agrawal Nikhil Chavan-Dafle Isaac Kasahara Selim Engin Jinwook Huh Volkan Isler
Robotic manipulation systems operating in complex environments rely on
perception systems that provide information about the geometry (pose and 3D
shape) of the objects in the scene along with other semantic information such
as object labels. This information is then used for choosing the feasible
grasps on relevant objects. In this paper, we present a novel method to provide
this geometric and semantic information of all objects in the scene as well as
feasible grasps on those objects simultaneously. The main advantage of our
method is its speed as it avoids sequential perception and grasp planning
steps. With detailed quantitative analysis, we show that our method delivers
competitive performance compared to the state-of-the-art dedicated methods for
object shape, pose, and grasp predictions while providing fast inference at 30
frames per second speed.