# DCSPose: A Dual-Channel Siamese Framework for Unseen Textures Object Pose Estimation
## 5 Discussion  
**Conclusions. ** Our novel network architecture DCSPose, employing dual-channel Siamese networks, effectively leverages multi-source data to address the challenge of pose estimation for unseen texture-less objects in industrial settings. It exhibits significant improvements compared to existing state-of-the-art methods. By employing a Siamese network structure, we achieved the capability to match both real-world and simulated scenes, consequently enabling effective object pose estimation using the template matching approach. Through the dual-channel design, we made full use of RGB and depth features from real scenes, effectively addressing the issue of limited features on textureless objects. The offline template library construction allowed us to rapidly extend estimation capabilities to new objects, simultaneously alleviating the time pressure associated with run-time processing. Our framework effectively addresses three major challenges posed by object pose estimation tasks in industrial applications, including handling textureless targets, ensuring model generalization, and meeting real-time processing requirements. **Future research directions. ** The template matching approach is intuitive and practical for addressing real-world problems. However, it does come with certain limitations. In the process of creating the template set, the angular sampling interval has an impact on the minimal error in the final matching results. If the sampling intervals are too large, even when matching the most similar template, it can result in substantial errors. On one hand, one can attempt to add post-processing through interpolation, aiming to make discrete parameter values as continuous as possible. On the other hand, increasing template density to reduce pose intervals is an option, though it might require a trade-off between time and computational costs. Through this work, we aim to drive the research in 3D pose detection towards practical applications, advancing the field of industrial automation. Simultaneously, we recognize the ongoing shortage of high-quality, large-scale 3D annotated datasets for industrial scenarios, which remains a pressing issue compared to 2D image datasets. Additionally, we observe that a considerable portion of current pose estimation research trends toward designing end-to-end network models. However, we believe that addressing real-world tasks can benefit from a multi-stage, modular design. Such an approach not only has the potential to reduce the demand for extensive 3D data annotation but also facilitates the system's rapid adaptation to various object types during practical applications. **Author Contributions:** Investigation, Z.Y. ; resources, L.L. ; writing--original draft preparation, Z.Y. ; writing--review and editing, Z.H., X.Y. and L.L. ; visualization, Z.Y. ; project administration, Z.H. ; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Shanghai Science and Technology Innovation Project (Grant No. XTCX-KJ-2023-75) and Science and Technology Service Network Initiative, Chinese Academy of Sciences (STS, Grant No. 20211600200102). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** We have created a brand-new set of simulation rendering templates for the T-LESS dataset, comprising RGB projections, depth maps, and mask images of 30 objects from 602 different viewpoints, totaling 54,180 images. For the convenience of future researchers, we have made this template data publicly available on Google Drive at [[https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) cVBPewNpUla6a6F64 wL6 QenitSVDIfd0H?usp=sharing]([https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) cVBPewNpUla6a6F64 wL6 QenitSVDIfd0H?usp=sharing) (accessed on 27 October 2023).