RealTime 6D Object Pose Estimation on CPU We propose a fast and accurate 6D object pose estimation from a RGBD image. Our proposed method is template matching based and consists of three main technical components, PCOFMOD (multimodal PCOF), balanced pose tree (BPT) and optimum memory rearrangement for a coarsetofine search. Our model templates on densely sampled viewpoints and PCOFMOD which explicitly handles a certain range of 3D object pose improve the robustness against background clutters. BPT which is an efficient treebased data structures for a large number of templates and template matching on rearranged feature maps where nearby features are linearly aligned accelerate the pose estimation. The experimental evaluation on tabletop and binpicking dataset showed that our method achieved higher accuracy and faster speed in comparison with stateoftheart techniques including recent CNN based approaches. Moreover, our model templates can be trained solely from 3D CAD in a few minutes and the pose estimation run in near realtime (23 fps) on CPU. These features are suitable for any real applications.

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.