Fovea3DPointRL is a research prototype that integrates foveated attention mechanisms, deep reinforcement learning, and LiDAR-based three-dimensional object detection into a cohesive PyTorch-driven workflow. In the proposed system, an RL agent first examines a low-resolution, global representation of the entire point cloud to inform its decision-making process. Based on this overview, the agent identifies the coordinates of a high-resolution “fovea” region. Points within that region are then dynamically cropped using GPU-accelerated Open3D operations and passed exclusively to a PV-RCNN detector. The agent’s reward function balances detection accuracy (measured by IoU or mAP) against computational cost; thus, the policy learned achieves a 30–50 % improvement in inference speed on standard benchmark datasets while maintaining minimal accuracy degradation.