In recent years, Robotic-Assisted Minimally Invasive Surgery (RMIS) has led to significant improvements in surgical precision and patient safety. Accurate 6D pose estimation of surgical tools is a fundamental enabler for several critical capabilities that enhance both human-in-the-loop and semi-autonomous interventions. The da Vinci Research Kit (dVRK) offers an open-source platform to study these tasks in both simulated and real environments. As part of an ongoing research, this thesis focuses on pose estimation as a key component in the automation of tasks such as suturing using the dVRK. This work investigates and compares two strategies for 6D pose estimation of dVRK instruments: a Marker-based approach and a Model-based, Marker-Less solution based on Deep Learning.The Marker-based method uses a printable cylindrical marker and the EPnP algorithm to compute 6D pose from 2D–3D correspondences.