Abstract: Many state-of-the-art grasping approaches are constrained to top-down grasps. Reliable robotic grasping in a human-centric environment requires considering all six degrees of freedom. We use an end-effector mounted depth camera to reconstruct the object’s surface by fusing data gathered along a shaped trajectory. The utilization of a truncated signed distance function and an effective pose refinement algorithm counteract typical sources of error. We propose to use a multi-view deep learning approach to vastly limit the search space of possible grasps and employ robust quality metrics to estimate their chances of success. To evaluate the performance of our approach we conducted extensive real-world experiments and achieved an average success rate of 92.3%.