### Related Works
#### 1.2.3 Deep Learning Approach  
Leveraging deep learning for 6 DoF pose estimation demonstrated considerable potential. This approach can be categorized into two main categories: (i) direct regression, which directly obtains the pose from an image input, and (ii) keypoint inference for 2D-3D correspondence map estimation using the deep-learning model and solving the perspective-n-point (PnP) problem to derive the pose.  
PoseCNN [21], an end-to-end pose estimation method, demonstrates remarkable robustness to occlusion. This network performs semantic segmentation and center-box prediction for translation and rotation prediction. It is trained directly on RGB images to predict the 6 DoF of predefined objects. Since its introduction, PoseCNN has led to numerous applications and has undergone various improvements [22; 23; 24; 25; 26]. It is model-specific, which means that it can only estimate the pose of the objects in the dataset on which it was trained. Thus, a dataset of RBG images and the corresponding precise poses of the tool heads are mandatory requirements. This is true for the majority of direct pose estimation models based on RGB images. Most deep learning 6 DoF pose estimators can provide a rough global pose that often requires subsequent local pose refinement. One such refinement technique is differentiable rendering [22], which calculates the loss by comparing an RGB image with a 3D image of the object and pose. This iterative process refines the pose in subsequent iterations. Alternatively, deep iterative matching (DeepIM) [23] receives the initial pose from PoseCNN and an RGB image to predict the differentiable manifold, allowing iterative refinement. Notably, this refinement process requires only an RGB image and an initial pose, which does not necessarily originate from PoseCNN. Hence, other direct pose estimation methods [27; 28; 29] can undergo the aforementioned refinement.  
The conventional 2D-3D correspondence method relies on the presence of textured objects. The limitation of requiring textures is addressed by learning-based 2D-3D methods, as researched by [PERSON] et al. [30]. On the one hand, the single-shot approach proposed by [PERSON] et al. [30] aims to predict nine points (eight corners of a 3D bounding box and one centroid), enabling the network to be 3D object-agnostic. On the other hand, PVNet [31] adopts a two-stage approach for pose estimation. In the first stage, PVNet regresses a 2D map of vectors whose directions are oriented toward the candidate keypoints. Utilizing these vectors to vote for keypoints, PVNet employs random sample consensus (RANSAC) to facilitate the voting process. This stage yields final 2D keypoints corresponding to the 3D keypoints of the object. In the second stage, PnP is computed to obtain an initial pose estimation. Similar to direct pose estimation, refinement can be performed after acquiring the pose using the 2D-3D correspondence method [24; 32]. [PERSON] et al. [33] affirm that robustness against occlusion can be enhanced through data augmentation, surpassing state-of-the-art techniques when the regression is coupled with a pose refiner. Despite the potential achievements of learning-based pose estimation, difficult generalization and dataset acquisition represent obstacles to wider adoption. Yet, synthetic photorealistic data, such as those produced by [PERSON] et al. [34], seem to be promising attempts at overcoming such limitations. Nevertheless, despite the rich scientific literature and although it holds great promise for the future of 6 DoF pose detection, deep learning-based techniques do not currently offer the level of millimetric precision and robustness required in the timber fabrication domain.