We recover high-frequency information encoded in the shadows cast by an object to estimate a hemispherical photograph from the viewpoint of the object, effectively turning objects into cameras. Estimating environment maps is useful for advanced image editing tasks such as relighting, object insertion or removal, and material parameter estimation. Because the problem is ill-posed, recent works in illumination recovery have tackled the problem of lowfrequency lighting for object insertion, rely upon specular surface materials, or make use of data-driven methods that are susceptible to hallucination without physically plausible constraints. We incorporate an optimization scheme to update scene parameters that could enable practical capture of real-world scenes. Furthermore, we develop a methodology for evaluating expected recovery performance for different types and shapes of objects.
We demonstrate a new lidar imaging method called bounce-flash lidar, in which the time-of-flight and intensity of two-bounce returns are used to estimate the depth and reflectance of points which are not illuminated directly.
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