Towards Pose Estimation of 3D Objects in Monocular Images via Keypoint Detection

The availability of a large number of crowdsourced CAD models of objects can be leveraged to solve the problem of pose estimation of 3D objects in monocular images. Convolutional Neural Networks(CNNs) perform to the best of their capability when they have been trained on a large amount of labeled data. We explore how 3D models can be used to generate lots of training images and annotations in the form of keypoint locations. We propose to use CNNs to first detect keypoints in rendered images. Once, we have a correspondence between 2D points in a test image and the 3D points on the CAD model, we can align 3D models in 2D images.