Yongzhi Su

"Multi-State Object Pose Estimation for AR Assisted Assembly"

The detection of objects in images and their classification into one of a number of predefined object classes has been one of the most researched topics in computer vision for several decades. A similar problem with specific constraints and challenges is object state estimation, dealing with objects that consist of several removable or adjustable parts. Automatic recognition of object state along with their pose directly from camera images can enable AR applications that assist in the assembly/disassembly and maintenance of these objects while increasing safety and human error detection and prevention.Traditionally, handcrafted features such as SIFT or HOG were used to train different types of classifiers for the task. However, industrial objects are usually textureless and such kind of features still have limited performance with them. The field was revolutionized by Deep Learning and Convolutional Neural Networks (CNNs), which can be trained to generate more complex features. The first task of my PhD is to design a CNN that is able to detect and regress the pose of an object in multiple states. In this talk, I will firstly give a brief overview of the CNN based object detection and object pose estimation methods. And then I will present our first result as well as the remaining challenges.