24 DAILY WACV Congrats, Doctor Stefano! Sunday Stefano Gasperini obtained his PhD just a few weeks ago. Under the supervision of Federico Tombari, he worked with BMW and the CAMP team at TUM, tackling reliability challenges in scene understanding for autonomous driving. Now a PostDoc advising PhD students, Stefano has also co-founded VisualAIs Labs GmbH with Shun-Cheng Wu. Their technology creates high-fidelity 3D renderings of e-commerce products from just smartphone images, making interactive 3D visualizations effortless. Congrats, Doctor Stefano! Imagine stepping into your car after a long night out—whether celebrating or finishing a CVPR submission—and letting it drive you home safely. Autonomous driving promises such a future, but making it reliable under all conditions poses significant challenges, from nighttime driving to unknown objects. While autonomous rides already exist in select areas, their widespread adoption is still on the horizon. Among the many obstacles, reliable scene understanding is a critical one, as the vehicles must perceive their surroundings accurately in all scenarios, including adverse weather, poor lighting, and entirely unseen objects. While much of the scientific community focuses on incremental benchmark improvements, Stefano’s PhD tackled fundamental reliability gaps often ignored. Take monocular depth estimation at night, for example. State-of-the-art self-supervised approaches fail in the darkness due to training assumptions that completely break with low light, reflections, and sensor noise, leading to extremely poor outputs. At ICCV 2023, Stefano and his colleagues introduced md4all, a method that leverages strong daytime models to improve nighttime depth estimation. Instead of retraining different models for every condition, md4all generates synthetic nighttime images corresponding to the available ones with good visibility and trains the model using supervision only from the original, well-lit data. The key is computing the losses only on the corresponding well-lit images, regardless
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