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- W4295206007 abstract "The Multipath effect in Time-of-Flight(ToF) cameras still remains to be a challenging problem that hinders further processing of 3D data information. Based on the evidence from previous literature, we explored the possibility of using machine learning techniques to correct this effect. Firstly, we created two new datasets of of ToF images rendered via ToF simulator of LuxRender. These two datasets contain corners in multiple orientations and with different material properties. We chose scenes with corners as multipath effects are most pronounced in corners. Secondly, we used this dataset to construct a learning model to predict real valued corrections to the ToF data using Random Forests. We found out that in our smaller dataset we were able to predict real valued correction and improve the quality of depth images significantly by removing multipath bias. With our algorithm, we improved relative per-pixel error from average value of 19% to 3%. Additionally, variance of the error was lowered by an order of magnitude." @default.
- W4295206007 created "2022-09-12" @default.
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- W4295206007 date "2015-12-13" @default.
- W4295206007 modified "2023-09-30" @default.
- W4295206007 title "Learning the Correction for Multi-Path Deviations in Time-of-Flight Cameras" @default.
- W4295206007 doi "https://doi.org/10.48550/arxiv.1512.04077" @default.
- W4295206007 hasPublicationYear "2015" @default.
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