# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 2 Related Works
### Pose Estimation
#### 2.1.1 Pose Estimation Methods Based on RGB Images  
RGB image-based 6D pose estimation methods are generally categorized into keypoint-based methods and direct regression methods. Unlike the traditional methods that use descriptor extraction algorithms to extract feature points from RGB images, recent methods leverage the powerful computational capabilities of deep learning to process RGB images and extract more representative feature points. For example, YOLO-6D [20] utilizes the YOLO framework to quickly match and project the predicted 3D bounding box of the target into a 2D image. By leveraging the natural 2D-3D keypoint correspondence, the PnP algorithm can be used to calculate a target's 6D pose. PVNet [21] extracts pixel-wisefeature vectors from RGB images using specific convolutional networks and then votes on these vectors to determine the object's keypoints. Finally, the PnP algorithm determines the 6D pose based on the keypoints with the highest votes. HybridPose [22] enhanced the robustness of pose estimation for symmetric objects by incorporating edge vectors and symmetry correspondences into the PVNet framework. In contrast, direct regression methods utilize network models to directly regress the 6D pose of the target object from the RGB image. For instance, in PoseCNN, semantic segmentation is first performed on the image, followed by a calculation of the translation and rotation for each segmented object, resulting in a more streamlined network structure. DeepIM [23] is an iterative refinement method for 6D object pose estimation based on deep learning. It first estimates an initial coarse 6D pose from an RGB image; then, it optimizes the initial pose by minimizing the difference between the observed and rendered images, iterating until the pose converges to a threshold or until a set number of iterations is reached. [PERSON] et al. [24] extended the DeepIM idea and proposed a more efficient multi-object pose estimation method called CosyPose by applying a rotation parameterization method, making convolutional neural network (CNN) training more stable.  
In addition to the aforementioned keypoint-based and direct regression methods, recent innovations in RGB-based 6D pose estimation have further improved the performance. CRT-6D [25] is a real-time 6D pose estimation method based on cascade regression trees, which strikes a good balance between computational speed and accuracy, making it well suited for real-time applications. However, it faces challenges in handling low-resolution and complex backgrounds. FS6D [26] focuses on few-shot 6D pose estimation for novel objects, making predictions with only a few support views without additional training and thus enhancing the model's generalization capability. However, FS6D is sensitive to the quality and quantity of support views, which can result in instability in diverse and complex scenes. RNNPose [27] uses recurrent neural networks (RNNs) to process data from consecutive frames, effectively capturing object motion information to improve the 6D pose estimation accuracy in dynamic scenes. Compared to traditional static image methods, RNNPose is more stable in continuous video scenes, but its high computational demands make it less applicable to real-time applications or limited hardware environments.  
RGB image-based 6D pose estimation methods feature simple inputs and fast processing, making them suitable for real-time applications. However, while recent innovations have improved their performance in few-shot learning and dynamic scenes, the robustness of these methods is generally limited when dealing with occlusions, complex backgrounds, and low-texture objects; thus, the pose estimation accuracy is also limited due to the lack of spatial geometry information.