# DCSPose: A Dual-Channel Siamese Framework for Unseen Textures Object Pose Estimation
## 4 Experiments  
### Experimental Setup  
**Dataset. ** We employed the T-LESS [42] dataset for our evaluation, as it contains 30 industry-relevant objects that lack significant texture or discriminative color. To assess the robustness of our model on unseen objects, we followed the evaluation criteria outlined in [9; 11; 26]. Specifically, we used objects 1-18 for training and reserved objects 19-30 as unseen objects to validate the model's generalization capability. The diversity in object shapes is illustrated in Figure 6, and there is a substantial visual contrast between seen and unseen objects. **Data preprocessing. ** In this work, the RGB and depth data used were captured by a structured-light RGB-D camera, Primesense CARMINE 1.09, which has an operation range of 0.35-3 m. To adapt to the differences between template and real-world depth images, preprocessing of depth maps is necessary. For template depth maps, the first step involves removing the background generated during rendering, followed by normalizing the object's depth values to a range of 0 to 255. As for the depth map of the query, the preprocessing does not consider the background. After removing invalid values such as holes, the depth was directly normalized to the range of 0 to 255. For facilitating performance comparisons across different models, object bounding boxes from the T-LESS dataset were employed to crop RGB and depth images, subsequently adjusted in width and height to fit a 224 \(\times\) 224 size. Similarly, for rendered template images, bounding boxes were obtained using binary masks of the templates and subsequently utilized to crop RGB and depth templates. This method facilitated the scaling of objects in both template and query images to approximate sizes, thereby mitigating discrepancies in object imaging sizes at different distances. Figure 6: Dataset division. Objects numbered 1 to 18 are allocated for training, while all objects are used for testing. Additionally, objects numbered 19 to 30 are utilized to assess generalization capabilities. **Data Augmentation. ** During the training process, to ensure the network can cope with interference from different backgrounds, random background and image augmentation techniques were applied to the RGB and depth data. This approach helps the network generalize effectively to various background conditions that may be encountered during real-world detection scenarios. Specifically, we performed image segmentation on the original dataset to remove the background, followed by the addition of random backgrounds with the aim of making the network adaptable to unknown and complex backgrounds. Additionally, we also conducted traditional data augmentation, which involved random adjustments to contrast, brightness, as well as Gaussian blur, sharpening, and other filtering operations. These aforementioned procedures were applied to both RGB and depth images. However, when processing the depth images, we omitted brightness-related adjustments, and the random backgrounds for depth images were grayscale. The input images used during training can be referenced in Figure 7.  
**Evaluation metrics. ** It is a common practice to use recall under the Visible Surface Difference (VSD, \(err_{vSD}\)) as evaluation metrics, particularly for objects with many symmetrical structures such as the T-LESS dataset. The VSD is an ambiguity-invariant pose error function that is determined by the distance between the estimated and ground truth visible object depth surfaces [26]. It follows that  
\[err_{VSD}=avg_{p\in\hat{V}_{0}\cup\overline{V}} \begin{cases}0\left(\begin{array} []{c}if\ p\in\hat{V}\cap\overline{V}\wedge\left|\hat{D}(p)-\overline{D}(p) \right|<\tau\\ otherwise\end{array}\right)\end{cases} \]  
where \(\hat{D}\) and \(\overline{D}\) are distance maps obtained by rendering the object model in the estimated pose and the ground-truth pose respectively, \(\hat{V}\) and \(\overline{V}\) are the visibility masks obtained by comparingthe depth maps.