The estimation of the pose of surgical instruments is important in Robot-assisted Minimally Invasive Surgery (RMIS) to assist surgical
navigation and enable autonomous robotic task execution. The performance of current instrument pose estimation methods deteriorates
significantly in the presence of partial tool visibility, occlusions, and changes in the surgical scene. In this work, a vision-based framework
is proposed for markerless estimation of the 6DoF pose of surgical instruments. To deal with partial instrument visibility, a keypoint object
representation is used and stable and accurate instrument poses are computed using a PnP solver. To boost the learning process of the model
under occlusion, a new mask-based data augmentation approach has been proposed. To validate our model, a dataset for instrument pose
estimation with highly accurate ground truth data has been generated using different surgical robotic instruments. The proposed network can
achieve submillimeter accuracy and our experimental results verify its generalisability to different shapes of occlusion.