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In this paper, we present a simple but powerful method to tackle the problemof estimating the 6D pose of objects from a single RGB image. Our system trainsa novel convolutional neural network to regress the unit quaternion, whichrepresents the 3D rotation, from the partial image inside the bounding boxreturned by 2D detection systems. Then we propose an algorithm we call BoundingBox Equation to efficiently and accurately obtain the 3D translation, using 3Drotation and 2D bounding box. Considering that the quadratic sum of thequaternion's four elements equals to one, we add a normalization layer to keepthe network's output on the unit sphere and put forward a special loss functionfor unit quaternion regression. We evaluate our method on the LineMod datasetand experiment shows that our approach outperforms base-line and some state ofthe art methods.

6D Object Pose Estimation Based on 2D Bounding Box