Nowadays service robots are entering more and more in our daily life. In such a dynamic environment, a robot frequently faces pile, packed, or isolated objects. Therefore, it is necessary for the robot to know how to grasp and manipulate various objects in different situations to help humans in everyday tasks. Most state-of-the-art grasping approaches addressed four degrees-of-freedom (DoF) object grasping, where the robot is forced to grasp objects from above based on grasp synthesis of a given top-down scene. Although such approaches showed a very good performance in predefined industrial settings, they are not suitable for human-centric environments as the robot will not able to grasp a range of household objects robustly, for example, grasping a bottle from above is not stable. In this work, we propose a multi-view deep learning approach to handle robust object grasping in human-centric domains. In particular, our approach takes a partial point cloud of a scene as an input, and then, generates multi-views of existing objects. The obtained views of each object are used to estimate pixel-wise grasp synthesis for each object. To evaluate the performance of the proposed approach, we performed extensive experiments in both simulation and real-world environments within the pile, packed, and isolated objects scenarios. Experimental results showed that our approach can estimate appropriate grasp configurations in only 22ms without the need for explicit collision checking. Therefore, the proposed approach can be used in real-time robotic applications that need closed-loop grasp planning.