The paper presents an economic framework for 6-DoF grasp detection, aiming to reduce resource costs while maintaining effective grasp performance. The authors identify dense supervision as a bottleneck in current state-of-the-art (SOTA) methods, leading to high training resource demands and convergence issues. To address this, they propose an economic supervision paradigm that includes a well-designed supervision selection strategy and an efficient training pipeline. This paradigm reduces the amount of supervision data, making training more feasible and converging faster. Additionally, the framework introduces a focal representation module, which focuses on specific grasps by incorporating an interactive grasp head and a composite score estimation module. Extensive experiments show that the proposed EconomicGrasp framework outperforms SOTA methods by about 3 AP on average, while significantly reducing training time, memory usage, and storage costs. The code for the framework is available at <https://github.com/iSEE-Laboratory/EconomicGrasp>.The paper presents an economic framework for 6-DoF grasp detection, aiming to reduce resource costs while maintaining effective grasp performance. The authors identify dense supervision as a bottleneck in current state-of-the-art (SOTA) methods, leading to high training resource demands and convergence issues. To address this, they propose an economic supervision paradigm that includes a well-designed supervision selection strategy and an efficient training pipeline. This paradigm reduces the amount of supervision data, making training more feasible and converging faster. Additionally, the framework introduces a focal representation module, which focuses on specific grasps by incorporating an interactive grasp head and a composite score estimation module. Extensive experiments show that the proposed EconomicGrasp framework outperforms SOTA methods by about 3 AP on average, while significantly reducing training time, memory usage, and storage costs. The code for the framework is available at <https://github.com/iSEE-Laboratory/EconomicGrasp>.