  We propose a fast and accurate 6D object pose estimation from a RGB-D image.
Our proposed method is template matching based and consists of three main
technical components, PCOF-MOD (multimodal PCOF), balanced pose tree (BPT) and
optimum memory rearrangement for a coarse-to-fine search. Our model templates
on densely sampled viewpoints and PCOF-MOD which explicitly handles a certain
range of 3D object pose improve the robustness against background clutters. BPT
which is an efficient tree-based 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 bin-picking dataset showed that our method achieved
higher accuracy and faster speed in comparison with state-of-the-art techniques
including recent CNN based approaches. Moreover, our model templates can be
trained only from 3D CAD in a few minutes and the pose estimation run in near
real-time (23 fps) on CPU. These features are suitable for any real
applications.
