# Region Pixel Voting Network (RPVNet) for 6D Pose Estimation from Monocular Image
## 1 Introduction  
Estimating the accurate 6D-Pose of certain objects has important implications in industries such as e-commerce and logistics [1], as pose information helps robotic systems to better manipulate materials. Monocular camera is one of the most common used sensors in pose estimation, due to its low cost, rich information and easy installation. However, since the 3D coordinates of spatial points cannot be obtained directly by a monocular camera, estimating the 6D-pose of an object from an image is a great challenge in research. If the prior 3D model of target object is given, the Perspective-n-Point (PnP)-like algorithms [2] can theoretically solve the problem. The correspondence between the points on the model and the points on the picture is firstly determined. These corresponding points are constrained by the rigid transformation from model to camera. Thus, the object's pose in the camera coordinate can be calculated. Traditional methods use handcraft features [3; 4; 5; 6] to detect the model's corresponding keypoints in the image. However, handcrafted features are not robust to changes in the environment. Template matching is also a common idea. Typical methods [7] extract prior templates from images of objects taken in various poses and estimates the poses of objects in image by matching thesetemplates. Similar to handcraft-features, such templates are sensitive to environmental change. With the development of deep learning, researchers try to dig the 6D-Pose information from 2D image in a machine learning way. In terms of network structure and function, machine learning methods can be roughly divided into two categories: end-to-end method and two-stage method. End-to-end methods try to predict the pose of target object directly from the input image. For example, PoseNet [8] uses Convolutional Neural Networks (CNN) to regress the 6D pose of camera directly. Since detection is a prerequisite for pose estimation, i.e., an object's pose cannot be predicted even before it is detected in the image, some studies have utilized results from the field of object detection. Based on Mask R-CNN [9], Deep-6 DPose [10] adds a CNN branch as pose regression head to predict pose. However, directly predicting object's position is difficult due to the large searching space and lack of depth information. By predicting the depth of the object center, PoseCNN [11] recovers the object's pose with its 2D localization in image. Some studies transform pose regression into classification problem. For example, SSD-6D [12], which is built up the SSD [13], uniformly discretizes the object's pose space with different viewpoints on a fixed radius. After the most probable rotation is classified, the translation is obtained by projection relations, and the final pose is obtained by refinement. A similar strategy of discretizing the pose space and predicting rotation by classification was adopted by [PERSON] et al. [14] and [PERSON] et al. [15]. Such discretization leads to coarse pose predictions and often requires additional refinement to obtain accurate pose. The prediction accuracy of end-to-end method in occluded scenes decreases, and in practice it generally requires mixing in occluded samples for training to improve. Two-stage methods generally predict the object's keypoints on the image and then estimate the object's pose with PnP algorithm. The method proposed in [16; 17] designs a YOLO-like [18] architecture to predict the locations of the projected vertices of the object's 3D bounding box. A similar approach was adopted by BB8 [19], which first finds the object's 2D center and then regresses the projection of the eight vertices of its 3D bounding boxes in the image with a CNN network. These methods do not accurately predict the keypoints in occluded scenes. The research on human keypoints detection is well developed [20; 21], and many studies are inspired by them. After finding the center point of an object, Centernet [22] regresses to the heatmaps for projections of 3D bounding boxes.