# DCSPose: A Dual-Channel Siamese Framework for Unseen Textures Object Pose Estimation
## 1 Introduction  
In recent years, there has been a growing demand for object pose estimation, and the utilization of deep learning technologies has significantly enhanced estimation accuracy. The emergence of large annotated datasets, such as those involved in BOP Benchmark [1; 2], has fostered research in this field. However, despite the considerable progress these researchers have made in areas such as robotics [3], autonomous driving [4], and augmented reality [5], significant challenges persist within the field of industrial applications. The most prominent challenge in industrial settings is the need to deal with a wide variety of objects. As a result, there is a heightened requirement for the algorithm's generalization capability, enabling rapid adaptation to new target objects. Moreover, current mainstream 3D pose estimation methods often necessitate extensive annotated data for training to achieve satisfactory estimating performance [6; 7]. However, annotating 3D pose data imposes higher demands in terms of accuracy, difficulty, and cost compared toannotating 2D data. These impediments limit the practical application of many research algorithms in industrial production. Furthermore, in industrial manufacturing, the objects to be processed frequently exhibit a lack of texture or possess minimal textural features, such as ceramics, resin, and metallic components. Pose estimation tasks for these objects find diverse applications in industry, such as object grasping, assembly, and stacking. The scarcity of texture features makes it challenging to extract sufficient information for accurate pose estimation through traditional feature detection methods. Low accuracy in target pose estimation can adversely affect various industrial applications. For instance, robots may fail to precisely determine the position and orientation of objects during grasping tasks, leading to unsuccessful grabs. Similarly, misalignment during assembly processes can result in component damage. Consequently, addressing the problem of pose estimation for textureless objects in industrial production scenarios remains a critical concern in this field [8]. Finally, systems applied in industrial production typically require sufficient real-time processing capabilities. Overly complex network designs or rendering and registration processes that demand extensive computational resources can jeopardize real-time performance. Therefore, proposing an efficient and accurate estimation framework that can adapt to industrial production is both necessary and challenging. The primary objective of this research was to address the three major challenges encountered in object pose estimation tasks within industrial settings, as described above. To reduce dependence on annotated data and meet the demands for generalization, an intuitive approach is template matching. Capturing multi-angle information of the objects to be detected in advance allows for the construction of a rich and scalable template library, where each template corresponds to a specific pose parameter. When real-time estimation is required, the on-site scene is compared with the template library to find the best-matching template. The pose parameters of the matching template are then used as the prediction result. If the process of building the template library can be automated, the system will be able to quickly adapt to new objects. Furthermore, by dividing the entire matching process into offline template set generation and online matching, computational resources and time costs during runtime processing can be reduced. Recent work has ingeniously tackled the task of pose estimation by utilizing template matching methods, leading to noteworthy results [9; 10; 11]. Template-based approaches primarily rely on computing the similarity between query images from real-world scenes and template images rendered from CAD models [12; 13].