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- W3204282185 abstract "Is it fundamentally possible to reconstruct a 3D scene from a purely temporal measurement? We define a purely temporal measurement as being made by a wide-field single-pixel bucket detector, viewing a scene illuminated uniformly, without scanning. Reconstructing a 3D scene from such data is strongly ill-posed, as a signal arriving time t d may have originated from not only a single point, but rather from any point ( x,y,z ) on the surface of a spherical dome defined by ( ct / 2 d 2) = x 2 + y 2 + z 2 . Yet, recent research has tackled this problem using strong priors regarding a fixed background, learned by a machine learning model [1] . This research limited itself to single-path echoes or in other words, the first reflection of waves from a scene. On the other hand, attempts at using multipath echoes - that is, data that contains waves that are reflected several times in the scene - without machine learning, constrained the reconstruction problem to simple scenes of a sparse set of points, reconstructable up to Euclidean congruence [2] . In this work, we demonstrate that using machine learning, multipath echoes of a scene provide an information-rich temporal trace, enabling high fidelity reconstruction of complex 3D scenes." @default.
- W3204282185 created "2021-10-11" @default.
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- W3204282185 date "2021-06-21" @default.
- W3204282185 modified "2023-09-26" @default.
- W3204282185 title "Inferring spatial scenes from their time-resolved multipath echoes" @default.
- W3204282185 doi "https://doi.org/10.1109/cleo/europe-eqec52157.2021.9542427" @default.
- W3204282185 hasPublicationYear "2021" @default.
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