Title: SilhoNet: An RGB Method for 3D Object Pose Estimation and Grasp Planning,
Abstract: Autonomous robot manipulation often involves both estimating the pose of the
object to be manipulated and selecting a viable grasp point. Methods using
RGB-D data have shown great success in solving these problems. However, there
are situations where cost constraints or the working environment may limit the
use of RGB-D sensors. When limited to monocular camera data only, both the
problem of object pose estimation and of grasp point selection are very
challenging. In the past, research has focused on solving these problems
separately. In this work, we introduce a novel method called SilhoNet that
bridges the gap between these two tasks. We use a Convolutional Neural Network
(CNN) pipeline that takes in ROI proposals to simultaneously predict an
intermediate silhouette representation for objects with an associated occlusion
mask. The 3D pose is then regressed from the predicted silhouettes. Grasp
points from a precomputed database are filtered by back-projecting them onto
the occlusion mask to find which points are visible in the scene. We show that
our method achieves better overall performance than the state-of-the art
PoseCNN network for 3D pose estimation on the YCB-video dataset.