This presentation explores a hybrid approach to bin picking that integrates deep learning techniques with rule-based algorithms. We begin by introducing the concept of Deep 3D matching and explain how it merges the strengths of both paradigms. The talk then covers the generation and use of synthetic training data to replace manual labeling processes. Finally, we discuss the system's architecture, emphasizing the role of 2D area cameras and the implications for implementation in real-world scenarios.