This work contributes to the robotic bin-picking problem, and more specifically to the problem of localizing piled box-like objects. We employ range imagery, and use box-like Superquadrics for modeling the target objects. Our approach for Superquadric segmentation is an extension of the widespread recover-and-select framework, which employs only region information and therefore suffers from the region over- growing problem. Our approach equally considers both region and boundary-based information for performing the recovery task. Extensive experimentation with a variety of target object configurations demonstrates that it outperforms the recover-and-select framework in terms of both robustness and computational efficiency. Moreover, if implemented in a parallel hardware environment, our approach can operate in real time.