# DCSPose: A Dual-Channel Siamese Framework for Unseen Textures Object Pose Estimation
## 2 Related Work  
### Pose Estimation  
Object pose estimation is a critical and valuable research area in the field of computer vision, with its primary objective being the identification of an object's position and orientation in three-dimensional space. Building upon the foundation of object detection tasks, pose estimation introduces more advanced technical requirements and holds the potential for a growing range of practical applications. This technology currently plays a pivotal role in a variety of application scenarios, and its demand is steadily increasing with the development of industrial automation and spatial computing technologies. Currently, in specific application domains, there has been notable research progress, such as in industrial automation [16; 17; 18], autonomous driving [19; 20; 21], and virtual and augmented reality [22; 23], among others. In the context of industrial automation, object pose estimation serves as a critical tool for assisting robots in determining the position and orientation of objects, enabling more precise grasping, assembly, or docking. In the field of autonomous driving, this technology contributes to enhanced environmental perception in vehicles, aiding in obstacle avoidance and route planning. In augmented reality scenarios, this technology can perceive the orientation of objects, facilitating object re-rendering and the application of special effects. Thanks to the advancements in deep learning, there has been a surge of exciting research in recent years in the field of object pose estimation. We can categorize deep learning methods for object pose estimation into the following types. **Regression Methods**: These methods use deep neural networks to directly regress the 6D pose of the camera or the objects [19; 24; 25]. The advantage of this approach is its speed in prediction. However, it requires a substantial amount of annotated data to train the network and is sensitive to noise and occlusions. Moreover, such methods are often useless when it comes to objects that have not been seen during training. **Retrieval or Classification Methods**: Drawing inspiration from template matching, these methods transform the pose estimation problem into a retrieval or classification task [9; 11; 26; 27]. This approach is robust against noise and occlusions but necessitates a large template library for matching. **Alignment or Registration Methods**: Such methods typically employ an iterative or mathematical approach for image alignment and point cloud registration, utilizing 2D projections or 3D point cloud representations for matching. The iterative process involves rendering, matching, or registration, ultimately yielding the pose parameters that fulfill the error criteria [28; 29; 30]. Deep learning methods have demonstrated excellent performance in many object pose estimation tasks. However, they also encounter several challenges and limitations, with the most prominent being the requirement for large volumes of annotated data. Unlike conventional object detection and classification tasks, collecting and annotating 6D pose data for objects is a costly and time-consuming endeavor. This significantly impedes the practical application of deep learning-based pose estimation algorithms. Some researchers have conducted research and practice on this problem. For instance, ref. [31] generated training data for object poses by constructing a photorealistic textured mesh model and rendering it from various perspectives and distances. Another significant challenge lies in the need for model generalization in practical applications. Deep learning models might encounter difficulties in maintaining their performance when faced with situations different from the training data, such as variations in lighting conditions, backgrounds, or viewpoints. Enhancing a model's performance for unseen objects is also a crucial concern in practical applications. Additionally, improving a model's generalization capability can also help reduce training costs.