A robust perception stack is critical, and relying on multiple sensors, such as LiDAR, radar, and cameras, ensures redundancy and accuracy. Having a reliable monocular 3D detection network acts as a safety net when other sensors fail due to harsh conditions, hardware faults, or unexpected inferences.We propose a methodology for cross-camera monocular 3D detection in the Autonomous Racing scenario.