# Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms
## 1 Introduction  
Accurate and efficient pose estimation of an Object-Of-Interest (OOI) is an important task in many robotic and computer vision applications involving vision-based robotic manipulation, position-based visual servoing, augmented reality, camera localization, etc. The purpose of object pose estimation is to recover the relative 3D pose of the OOI in the camera coordinate frame from captured scene images. This problem is one of the fundamental research topics in computer vision, and a variety of approaches has been proposed in the literature. Traditionally, the object pose estimation techniques can be divided into feature-based and model-based approaches. The feature-based approaches use geometric features, such as points, lines or circles, to estimate the 3D pose of the OOI from the scene image. In contrast, the model-based approaches use a priori-constructed 3D model of the OOI to deal with object detection and pose estimation issues efficiently. Empirically, the model-based approaches provide better robustness than the feature-based ones in a heavily-cluttered environment, but they usually cost more computational efforts in computing the optimal pose of the OOI. Feature-based approaches detect the OOI and estimate its 3D pose with respect to (w.r.t.) the camera based on the keypoints, which are image features robust to translation, scaling and rotationtransformations [1; 2; 3; 4]. In the keypoint-based approaches, a keypoint matching operation is required to detect the keypoint correspondences between an OOI template image and the input image. Then, the 3D pose of the OOI is recognized using a keypoint classifier [5; 6], which was trained from a large training dataset with different views of the OOI. By doing so, the pose estimation problem is simplified as a keypoint recognition problem, which can be efficiently resolved using randomized trees. The authors in [7] proposed a direct pose estimation approach, which can reconstruct 3D data directly from the images based on speeded-up robust features (SURF) [2]. On the other hand, model-based approaches use a 3D CAD model to estimate the 3D pose of the OOI detected in the image. In [8], [PERSON] and [PERSON] proposed a real-time attitude and position determination (RAPiD) [9] style tracking system, which consists of a global and a local pose estimation unit. The global pose estimation unit aims to detect the OOI while determining its initial pose w.r.t. the camera based on a keypoint-based object recognition method. Next, the local pose estimation unit refines its 3D pose based on CAD model-based back-projection optimization via an edge-based pose tracking algorithm. In [10], [PERSON] et al. proposed a probabilistic pose estimation approach to resolve the perspective-n-line problem from a set of noisy matched 3D model and 2D image lines. The authors in [11] proposed a model-based contour fitting algorithm, which estimates the optimal 3D pose of the OOI by fitting contour lines between the projected CAD model and detected contour lines of the target. Recently, the authors in [12] proposed a simultaneous 3D object recognition and pose estimation algorithm, which recognizes the OOI while estimating its 3D pose based on the 3D keypoint matches between the scene and the model point clouds. Another category of model-based pose estimation methods commonly used in practice is Homography Decomposition (HD) methods that simplify the 3D model of the OOI as a planar model in the 3D workspace. Based on this planar model constraint, the HD methods directly decompose the plane-to-image perspective projection matrix into the relative rotation and translation information w.r.t the camera. The conventional HD method works with the fact that the projection of a plane onto the image plane is a homography. This fact provides an efficient way to extract the extrinsic parameters of the camera in camera calibration [13].