Methodology reveals a safety net for 3D detection using sensors under harsh conditions in autonomous racing.
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.
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Elena et al. (2025) studied this question.
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