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- W3011763694 abstract "Building good 3D maps is a challenging and expensive task, which requires high-quality sensors and careful, time-consuming scanning. We seek to reduce the cost of building good reconstructions by correcting views of existing low-quality ones in a post-hoc fashion using learnt priors over surfaces and appearance. We train a CNN model to predict the difference in inverse-depth from varying viewpoints of two meshes -- one of low quality that we wish to correct, and one of high-quality that we use as a reference. In contrast to previous work, we pay attention to the problem of excessive smoothing in corrected meshes. We address this with a suitable network architecture, and introduce a loss-weighting mechanism that emphasises edges in the prediction. Furthermore, smooth predictions result in geometrical inconsistencies. To deal with this issue, we present a loss function which penalises re-projection differences that are not due to occlusions. Our model reduces gross errors by 45.3%--77.5%, up to five times more than previous work." @default.
- W3011763694 created "2020-03-23" @default.
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- W3011763694 date "2020-01-01" @default.
- W3011763694 modified "2023-09-28" @default.
- W3011763694 title "Learning Geometrically Consistent Mesh Corrections" @default.
- W3011763694 doi "https://doi.org/10.5220/0008947806640675" @default.
- W3011763694 hasPublicationYear "2020" @default.
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