Estimating a room geometry using multiple microphones rises an echoes labeling problem. Two recent methods called the graph-based and the subspace-greedy methods have shown their capability in solving this problem. The graph-based method attains a good accuracy but suffers in maintaining the computational cost when the number of microphones is larger than 7. On the other hand, the subspace-greedy method provides suboptimal accuracy with much lower computational time. Here we construct the hybrid combination methods using those two baseline methods by interchanging their intermediate steps: the refinement step and the source localization step. To assess their practicability in a real-life application such as virtual reality games and robot navigation, the performance of these hybrid methods were tested against the close microphones arrangement on the sphere's surface. However, this new microphones' constellation brings up a low dimensional problem. To deal with this matter, we use the weighted least squares as the source localization procedure. Finally, experiments on synthetic squared distance data demonstrate the feasibility of all hybrid methods for estimating the room geometry with centimeter precision within seconds.