1.0Monocular Vision for Obstacle Detection in Autonomous Aircraft Operationshttps://mscvprojects.ri.cmu.edu/2026teamf12Results - Monocular Vision for Obstacle Detection in Autonomous Aircraft Operationsrich600338<blockquote class="wp-embedded-content" data-secret="7orkd30MKa"><a href="https://mscvprojects.ri.cmu.edu/2026teamf12/results/">Results</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://mscvprojects.ri.cmu.edu/2026teamf12/results/embed/#?secret=7orkd30MKa" width="600" height="338" title="“Results” — Monocular Vision for Obstacle Detection in Autonomous Aircraft Operations" data-secret="7orkd30MKa" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script>
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Relative Depth Model Validation To validate model performance on real-world aerial datasets, we conducted inference using off-the-shelf depth models on captured drone flight data. An analysis of the ground truth distribution and signed relative error residuals was performed to characterize model bias. The results indicate an overall under-prediction bias in relative depth models; however, the … Continue reading ""https://mscvprojects.ri.cmu.edu/2026teamf12/wp-content/uploads/sites/154/2026/05/image.png2048846