Largescale 6D Object Pose Estimation Dataset for Industrial BinPicking In this paper, we introduce a new public dataset for 6D object pose estimation and instance segmentation for industrial binpicking. The dataset comprises both synthetic and realworld scenes. For both, point clouds, depth images, and annotations comprising the 6D pose (position and orientation), a visibility score, and a segmentation mask for each object are provided. Along with the raw data, a method for precisely annotating realworld scenes is proposed.To the best of our knowledge, this is the first public dataset for 6D object pose estimation and instance segmentation for binpicking containing sufficiently annotated data for learningbased approaches. Furthermore, it is one of the largest public datasets for object pose estimation in general. The dataset is publicly available at http:www.binpicking.aien dataset.html.

PPRNet:Pointwise Pose Regression Network for Instance Segmentation and 6D Pose Estimation in Binpicking Scenarios Accurate object 6D pose estimation is a core task for robot binpicking applications, especially when objects are randomly stacked with heavy occlusion. To address this problem, this paper proposes a simple but novel Pointwise Pose Regression Network (PPRNet). For each point in the point cloud, the network regresses a 6D pose of the object instance that the point belongs to. We argue that the regressed poses of points from the same object instance should be located closely in pose space. Thus, these points can be clustered into different instances and their corresponding objectsxe2x80x99 6D poses can be estimated simultaneously. In our experiments, PPRNet outperforms the stateoftheart approach by 15%  41% in average precision when evaluated on the benchmark Sileane dataset. In addition, it also works well in real world robot binpicking tasks.