  Estimating 6D poses of objects is an essential computer vision task. However,
most conventional approaches rely on camera data from a single perspective and
therefore suffer from occlusions. We overcome this issue with our novel
multi-view 6D pose estimation method called MV6D which accurately predicts the
6D poses of all objects in a cluttered scene based on RGB-D images from
multiple perspectives. We base our approach on the PVN3D network that uses a
single RGB-D image to predict keypoints of the target objects. We extend this
approach by using a combined point cloud from multiple views and fusing the
images from each view with a DenseFusion layer. In contrast to current
multi-view pose detection networks such as CosyPose, our MV6D can learn the
fusion of multiple perspectives in an end-to-end manner and does not require
multiple prediction stages or subsequent fine tuning of the prediction.
Furthermore, we present three novel photorealistic datasets of cluttered scenes
with heavy occlusions. All of them contain RGB-D images from multiple
perspectives and the ground truth for instance semantic segmentation and 6D
pose estimation. MV6D significantly outperforms the state-of-the-art in
multi-view 6D pose estimation even in cases where the camera poses are known
inaccurately. Furthermore, we show that our approach is robust towards dynamic
camera setups and that its accuracy increases incrementally with an increasing
number of perspectives.
