Infrastructure-based perception plays a crucial role in intelligent transportation systems by providing global situational awareness and enabling cooperative autonomy. However, camera-based 3D object detection remains challenging under multi-view infrastructure setups, heterogeneous camera configurations, degraded sensing conditions, diverse road layouts, and adverse environmental conditions. We introduce MIC-BEV, a Transformer-based bird's-eye-view (BEV) perception framework for infrastructure-based multi-camera 3D object detection. MIC-BEV supports a variable number of cameras with heterogeneous intrinsic and extrinsic parameters and uses a graph-enhanced fusion module to adaptively integrate multi-view image features by modeling geometric relationships between cameras and the BEV grid. To support training and evaluation, we introduce M2I, a synthetic dataset featuring diverse camera layouts, road geometries, environmental conditions, and sensor-degradation cases. Experiments on the simulated M2I dataset and the real-world RoScenes dataset show that MIC-BEV achieves strong 3D detection performance and improved robustness under challenging conditions. Ablation studies validate the effectiveness of its key design choices, while weight analyses show that the learned fusion weights are associated with view-reliability factors and adapt to degraded camera inputs. We further report computational cost and deployment-related factors, providing additional evidence of practical feasibility and real-world deployment potential. These results indicate that MIC-BEV is a promising framework for infrastructure-based perception. The dataset and source code are available at: https://github.com/HandsomeYun/MIC-BEV.

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