In knee arthroplasty, computer-assisted navigation enhances the accuracy of prosthesis placement. However, current methods rely on invasively drilled locators to track the knee position during surgery, prolonging the healing process. For this reason, research is focused on markerless approaches capable of determining knee orientation and transferring preoperative planning into the surgical environment. This work presents a trinocular multi-object 3D reconstruction system designed for intraoperative acquisition of the knee surface, providing a foundation for marker less navigation. Due to the scarcity of real surgical data with ground truth, a synthetic dataset was created using Blender to simulate optical image acquisition of a virtual knee model under controlled camera and lighting conditions. The dataset enables a systematic evaluation of how camera motion and viewpoint affect pose estimation and 3D reconstruction accuracy. The results demonstrate that moderate camera deflection between 15° and 25° achieve the best balance between accurate camera pose estimation and surface reconstruction quality. The work confirms the potential of trinocular SLAM for robust bone surface tracking while also identifying the limitations of synthetic data, such as the absence of real-world visual variability. These results form the basis for future work on 3D reconstruction during dynamic knee movements and their tracking, as well as on the integration of markerless optical navigation systems into surgery.