SilhoNet: An RGB Method for 6D Object Pose Estimation
This addresses the problem of enabling autonomous robot manipulation in cost-sensitive or constrained environments where RGB-D sensors are unavailable, representing an incremental improvement over existing monocular methods.
The paper tackles 6D object pose estimation from monocular RGB images, a challenging problem due to cost or environmental constraints limiting RGB-D sensor use, and introduces SilhoNet, which predicts pose via silhouette representations and achieves better overall performance than state-of-the-art methods on the YCB-Video dataset.
Autonomous robot manipulation involves estimating the translation and orientation of the object to be manipulated as a 6-degree-of-freedom (6D) pose. Methods using RGB-D data have shown great success in solving this problem. 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, the problem of object pose estimation is very challenging. In this work, we introduce a novel method called SilhoNet that predicts 6D object pose from monocular images. We use a Convolutional Neural Network (CNN) pipeline that takes in Region of Interest (ROI) proposals to simultaneously predict an intermediate silhouette representation for objects with an associated occlusion mask and a 3D translation vector. The 3D orientation is then regressed from the predicted silhouettes. We show that our method achieves better overall performance on the YCB-Video dataset than two state-of-the art networks for 6D pose estimation from monocular image input.