Real-Time 6D Object Pose Estimation on CPU
This enables real-time pose estimation for applications like robotics and bin-picking, though it is incremental as it builds on template matching approaches.
The paper tackles 6D object pose estimation from RGB-D images by proposing a template matching method with three components (PCOF-MOD, balanced pose tree, and memory rearrangement), achieving higher accuracy and faster speed (23 fps on CPU) compared to state-of-the-art techniques.
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.