# DCSPose: A Dual-Channel Siamese Framework for Unseen Textures Object Pose Estimation
## 1 Introduction
By constructing template sets using CAD models, these methods inject new object features into the neural network model, ensuring it retains sufficient matching capabilities when confronted with novel objects. However, previous work has not demonstrated satisfactory performance when dealing with textureless objects. Textureless objects tend to provide less texture and geometric feature information [14; 15]. It has been observed that relying solely on RGB information is insufficient to achieve optimal matching results. RGB information is also susceptible to environmental lighting conditions, leading to challenges in maintaining consistent performance when there are significant differences between training and testing scenes. The reduced cost and increased availability of industrial depth cameras have made it more convenient to obtain depth information for objects in industrial settings. Distinguishing itself from RGB, depth data can reflect the surface geometry of the target object. This capability aids in distinguishing between different poses of textureless objects, thus generating valuable data. Therefore, utilizing depth information for feature extraction and fusion is a viable approach to explore. An analysis of real-world industrial scenarios and previous research results prompted us to design a novel template matching approach that leverages multiple types of data. We constructed a template set comprising predefined pose parameters, encompassing both RGB images and depth maps, generated with high-fidelity rendering using CAD models. Subsequently, we employed a dual-channel feature extractor to obtain fused features from RGB and depth data, followed by training using a Siamese network structure. Finally, by computing the similarity between features, we identified the template that best matched the real-world scene, enabling us to estimate the object's pose, as illustrated in Figure 1. To facilitate visualization, the depth map is displayed in pseudo-color format. This approach facilitates the alignment of real-world images with rendered templates, harnessing multidimensional features from both RGB and depth data to enhance the comprehensiveness and robustness of pose estimation. Moreover, our method demonstrates high versatility when handling object categories unseen during the training phase. By simply providing the CAD model of a new object, the network's matching capabilities can be transferred through the automated construction of templates for the new object. We conducted extensive experiments on the designed network framework. The experimental results demonstrate that our pose estimation framework, DCPSose, effectively enhances the pose estimation of novel textureless objects. The dual-channel structure enables feature extraction from multiple sources while preventing premature feature fusion from diverse origins. Through automated template construction and employing contrastive learning, the dependence on annotated data from real scenes can be reduced. Due to the adoption of a template matching method with separate offline and online stages, our framework exhibits strong generalization and real-time processing capabilities. We summarize the contributions of this work as follows:  
1. To address the need for pose estimation of unseen textureless objects in industrial settings, we introduced a novel network architecture. This network comprises two distinct feature extraction branches, one focused on RGB features and the other on depth features. Utilizing a contrastive learning approach, we learned the correlation between real and simulated data from these two feature types. Our joint feature learning framework effectively enhances the performance of pose estimation for unseen textureless objects. 2. To facilitate the matching of depth features, we generated a new template set based on T-LESS using CAD models. This offline-constructed template set comprises RGB projections and depth maps of objects from T-LESS at various viewpoints.