# A Novel Metric-Learning-Based Method for Multi-Instance Textureless Objects' 6D Pose Estimation
## 1 Introduction  
Estimating 6D pose, i.e., 3D translation and 3D rotation of a target, is a fundamental problem in intelligent manufacturing, especially in the application fields of object grasping [1, 2], assembling [3, 4], bin-picking [5, 6], and stacking [7] with the help of the visual sensors. Visual sensors in an industrial environment can mainly be divided into three categories, namely, RGB, D, and RGB-D sensors. RGB sensors only achieve color information through a CMOS unit. D sensors use structured light, lidar injector-receiver, or radar injector-receiver to measure the distance from the camera to the target. RGB-D sensors combine both RGB and D sensors and leverage the calibration method to assign color information onto the depth information. However, there are limitations for D sensors in industrial environments [8]. On one hand, using depth sensors in industrial environments are not always useful, as there are plant of non-Lambert surface objects such as metal parts, glasses, and ceramics, which have uncertain reflection ratios for the light to make the depth immeasurable. On the other hand, thanks to the fast development of the deep learning technologies in recent years, the performance of 6D pose estimation methods using only RGB information is comparable with those using RGB-D information [9, 10]. Therefore, we focus on the investigation of RGB-based 6D pose estimation method in this paper. Traditional methods use different kinds of hand-crafted descriptors [11, 12, 13] to extract features surround the image points to establish the feature descriptions of the image points. The property of scale and rotation invariant is always considered to ensure the feature similarity of the same point of an object under different point-of-view in the image. These methods are sufficient for rich textured objects because of the variant color gradients of their surfaces; however, they are not capable of obtaining distinguishable point features from textureless surfaces such as metal, glasses, and ceramics. To solve this problem, geometric features such as lines [14, 15], moments [16], circles [17], and gradients of edges [18, 19], which can represent the geometric structures of an object, have been designed to describe the implicit features. Properties that are invariant to scale and rotation have also studied onthese geometric features [20]. However, the geometric features usually describe the overall structure of an object. When they are invariant to rotation and scale, they are only useful in object detection from an image, but lose the ability to distinguish different translation and rotation of the object. With the fast development of deep learning technologies in recent years, many researchers have used deep neural networks to predict the 6D pose of a textureless object. SSD-6D [10] uses a direct regression strategy to predict a translation and orientation based on the popular SingleShot multibox Detector (SSD) object detection framework. DeepIM [21] proposes a CNN structure to iteratively measure the difference between the current 2D image projection of the predicted pose and the real 2D image. A deep neural network that outputs the optic flow between the two images was designed to provide pose refinement for the current pose. [22] combines semantic key-points predicted by a convolutional network with a deformable shape model to determine the 2D-3D correspondences. PVNet [9] regresses pixelwise vectors pointing to the key-points with a modified U-Net structure and proposes a voting scheme to decide the location of the key-points. HybridPose [23] extends the approach of PVNet [9] by utilizing a hybrid intermediate representation to express different geometric information in the input image, including key-points, edge vectors, and symmetry correspondences. CosyPose [24] develops a robust method for matching individual 6D object pose hypotheses across different input images in order to jointly estimate camera viewpoints and 6D poses of all the objects in a single consistent scene.