Reflecting on the growing emphasis on the role of explainability in research, Bailey says it is a trade-off: “You need to push forward and figure out what works in practice, and then you need to step back and ask, ‘Why do these methods work so well?’ It’s a constant process moving back and forth between the two.” In addition to his work on stochastic geometry, Bailey is exploring Monte Carlo PDE solving, which involves adapting Monte Carlo methods that work really well for light transport and simulating light to other types of physics, such as heat transfer, acoustics, and wave equations. “I don’t think this has been as present in the vision community yet, but it’s been starting to gain some attention in graphics,” he tells us. “I think, eventually, these algorithms will be of interest in the computer vision community because we’re seeing the development of all sorts of new imaging modalities or renewed interest in modalities like thermal imaging. Good ways to simulate those should help vision researchers and practitioners develop algorithms that work with physics beyond just light.” Looking ahead, Bailey is excited about the potential for developing this work further, including extending the stochastic geometric approach to a broader range of stochastic models and probabilistic assumptions about the world or scenes. Also, the core idea of stochastic geometry has applications beyond light transport algorithms, which opens a range of possibilities for future research. Could we be sensing the first hints of next year’s award paper? “I’d love that, but I’m happy with the one this year for now!” he laughs. “I feel very fortunate to have had our paper selected. I hope everyone who reads it enjoys it and takes something away from this stochastic geometry perspective.” To learn more about Bailey’s work, visit Oral Session 1B: Vision and Graphics (Summit Flex Hall AB) from 9:00 to 10:30 [Oral 4] and Poster Session 1 & Exhibit Hall (Arch 4A-E) from 10:30 to 12:00 [Poster 412]. 7 DAILY CVPR Wednesday Objects as Volumes
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