## 2 Developed Methodology  
In this section, we present the methodology developed for TTool which aims to precisely determine the 6 DoF pose of a given tool head. Achieving accurate 3D localization of end-effector results is crucial for advanced augmented fabrication in AR manufacturing applications. The integration of TTool into any subtractive AR manufacturing system enables the computation of pertinent information to guide users with machine-like precision during any woodworking operation that requires tools. Nevertheless, challenges abound in the 3D localization of metal end effectors, particularly in augmented woodworking involving power tools. These challenges include the effectiveness and symmetry of most toolheads, the skewed and often occluded camera view caused by the onboard mounted camera, as well as environmental factors (such as vibrations), noise in unstructured settings (such as construction sites or workshops), and varying lighting conditions. The designated TTool's sensor is a monocular RGB camera with a fish-eye lens, as illustrated in Figure 1a. This ordinary sensor was selected due to its economic availability, versatility, and potential for broader adoption. Additionally, the designated monocular RGB sensor is designed to be affixed in a stationary yet adjustable position on the tool. To address this requirement,we devised a magnetic mounting system between the sensor and the tools, allowing for easy interchangeability in the toolset.  
To tackle these challenges, we devised a robust 6 DoF pose detector implemented as a multistep human-supervised pipeline. The paramount focus of TTool's development was ensuring robustness. To achieve this objective, we employed a combination of human  
Figure 1: Early developments of TTool and circular saws: (**a**) monocular RGB camera, (**b**) circular saw blade, and (**c**) camera live feed augmented with TTool’s detection widgets. Since the blade’s pose is punctually detected, the targeted object can later be obstructed (e.g., here, through blade protection) without interfering with the tracking.  
inputs, ML classification, and traditional computer vision techniques within the same software framework. TTool consists of a sequential process, illustrated in Figure 2, involving the following steps: (1) a custom-trained ML classifier detects the tool head type in the field, loading its previously scanned 3D mesh at runtime; (2) the user employs a built-in transformation system to manipulate the visualized 3D model, approximating an initial global pose input; (3) a local refiner optimizes the initial pose to achieve precise object locking between the physical tool and its 3D model counterpart; (4) the user confirms the refinement by visually checking the alignment, and the validated pose is cached, allowing for the fabrication process to commence. While drilling or cutting, even when the tool head is occluded by the timber element, TTool--owing to the stationary position of the monocular camera--can still provide object localization (refer to Figure 3b). Ultimately, a 6 DoF detector, such as TTool in AR manufacturing, proves valuable by offering computed feedback, such as depth or orientation, which would otherwise be extremely challenging for the human eye to discern with accuracy.  
Figure 2: Screenshots from each phase of the TTool pipeline for (**a**) drill bits and (**b**) blades. (1) The tool is manually inserted, but the corresponding 3D model is not yet paired. (2) The ML classifier detects the tool, and the loaded model is manipulated by the user to a satisfactory initial pose. (3) TTool’s refiner optimizes the input pose until the user decides to interrupt the operation. Finally, (4) the pose is validated, and the 3D model is locked in place, becoming resilient to obstructions and the dynamic background.  
The following subsections delve into the details of each phase and the components involved in the functioning of TTool.