# Texture-Less Shiny Objects Grasping in a Single RGB Image Using Synthetic Training Data
## 1 Introduction  
Industrial metal parts are necessary components of many products. Therefore, it is essential to facilitate computers to recognize and locate industrial metal parts in tasks like pick-and-place and assembly. Estimating their pose is vital for robotic grasping. However, this issue is challenging because of the dynamic changes in the representation of metal parts. The surface of metal parts is texture-less and shiny, which poses a considerable challenge for multiple traditional pose estimation approaches, owing to their dependence on reliable visual features. The illumination, occlusions, and clutter between objects may affect the actual appearance of the industrial parts. Figure 1 shows the challenges of estimating the pose of shiny metal parts: the light may cause the metal parts to have specular highlights; the cluttered metal parts may occlude with each other; metal parts with low-light may drive extreme darkness and the metal parts may induce interreflection with others. Some pose estimation approaches (e.g., [1,2]) require the depth information retrieved by actively projecting coded patterns, which are ineffective for shiny metal parts because the projected patterns may have a misleading reflection or may be undetectable. Thus, designing a strategy that does solely depend on RGB images is essential. Traditionally, researchers estimate the 6D object pose using object features (e.g., keypoints, color and silhouette). In robotic bin-picking applications, the perception of texture-less, highly reflective parts is a valuable but challenging task. The high glossiness can introduce fake edges in RGB images and inaccurate depth measurements [3]. The active lighting strategies may improve the pose estimation performance. Still, the metal parts are stacked together, and the lights have to change according to the surroundings of the metal parts, so it is not robust to the problem. The active lighting strategies will lead to extra cost. In addition, in some application scenes, like sorting surgical tools, the background is a shiny metal plate which poses a challenge to the estimation problem. Last, the labeled data is not only used in the object detection but also keypoint detection. It is difficult to label keypoint accurately in the real data, so we propose a method to use synthetic images with labeled keypoints. Hence, it retains the limitations of texture-less metal parts. Recently, deep learning has proved its effectiveness among computer vision techniques because it is trained on real images or synthetic data. It is therefore more accurate and robust than traditional feature-based methods. However, it is influenced by training data which influence their proformances and require intensive manual labor to mark the datasets. The OHEM [4] is a good algorithm for object detection. Still, it is not suitable for the keypoints detection since we need to label the semantic keypoints accurately, which is a challenging task for real images. To overcome this problem, some researchers have used synthesizing datasets [5; 6; 7] and produced ground truth labels [8; 9]. Some researchers use unlabeled data [10; 11] in object detection, but the methods are not applicable to our keypoints detection problem. Using deep learning approaches to estimate the 6D pose of an object requires training samples, which include objects with accurate annotations. It is expensive to annotate  
Figure 1: Challenges of Pose Estimation for Shiny Metal Parts. them using human labor. To solve this problem, we propose a novel strategy for 6D pose estimation using synthetic training data. The proposed system takes an RGB image as input to detect shiny metal parts, and subsequently uses semantic keypoint detection to extract the features, a novel method for interpreting the detected edges or skeleton in the semantic keypoints detection. Particularly, our approach does not count on continuous contours, but explores distinct combinations of dense discrete points along the edges.