  In the industrial domain, the pose estimation of multiple texture-less shiny
parts is a valuable but challenging task. In this particular scenario, it is
impractical to utilize keypoints or other texture information because most of
them are not actual features of the target but the reflections of surroundings.
Moreover, the similarity of color also poses a challenge in segmentation. In
this article, we propose to divide the pose estimation process into three
stages: object detection, features detection and pose optimization. A
convolutional neural network was utilized to perform object detection.
Concerning the reliability of surface texture, we leveraged the contour
information for estimating pose. Since conventional contour-based methods are
inapplicable to clustered metal parts due to the difficulties in segmentation,
we use the dense discrete points along the metal part edges as semantic
keypoints for contour detection. Afterward, we exploit both keypoint
information and CAD model to calculate the 6D pose of each object in view. A
typical implementation of deep learning methods not only requires a large
amount of training data, but also relies on intensive human labor for labeling
the datasets. Therefore, we propose an approach to generate datasets and label
them automatically. Despite not using any real-world photos for training, a
series of experiments showed that the algorithm built on synthetic data perform
well in the real environment.
