We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot technique for this task  that only predicts an approximate 6D pose that must then be refined, ours is accurate enough not to require additional post-processing. As a result , it is much faster-50 fps on a Titan X (Pascal) GPU-and more suitable for real-time processing. The key component of our method is a new CNN architecture inspired by [28, 29] that directly predicts the 2D image locations of the projected vertices of the object's 3D bounding box. The object's 6D pose is then estimated using a PnP algorithm. For single object and multiple object pose estimation on the LINEMOD and OCCLUSION datasets, our approach substantially outperforms other recent CNN-based approaches [11, 26] when they are all used without post-processing. During post-processing, a pose refinement step can be used to boost the accuracy of these two methods, but at 10 fps or less, they are much slower than our method.
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