Abstract
3D pose estimation is a key component of many important computer vision tasks like autonomous navigation and robot manipulation. Current state-of-the-art approaches for 3D object pose estimation, like Viewpoints & Keypoints and Render for CNN, solve this problem by discretizing the pose space into bins and solving a pose-classification task. We argue that 3D pose is continuous and can be solved in a regression framework if done with the right representation, data augmentation and loss function. We modify a standard VGG network for the task of 3D pose regression and show competitive performance compared to state-of-the-art.
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