Object pose estimation using category information from a single image

3D object pose estimation is one of the most important challenges in the field of computer vision. A huge amount of image resources such as images on the web or photos taken before can be utilized if the system can estimate the 3D pose from a single image. On the other hand, the object's category and position on the image can be estimated by using state of the techniques in the general object recognition. We propose a method for 3D pose estimation from a single image on the basis of the known object category and position. We employ Regression Forests as the machine learning algorithm and HOG features as the input vectors. The regression function is created based on HOG features which express the differences in shapes depending on the viewing directions and corresponding poses. We evaluate the accuracy of pose estimation by using multiple objects with different categories.