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- W3107084498 abstract "We propose a photometric stereo method using Convolutional Neural Network (CNN) based method, which is effective for deriving surface normal data from non-lambertian objects. Our method extracts feature maps from a set of images of object using shared feature extraction network, and merge the extracted feature maps using two pooling method: max-pooling and average-pooling. The merged feature maps are concatenated and passed to final CNN layers to derive the surface normal map. We tested our network on the most widely-used benchmark dataset and confirmed that our method performs better than existing deep learning based photometric stereo method." @default.
- W3107084498 created "2020-12-07" @default.
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- W3107084498 date "2020-10-13" @default.
- W3107084498 modified "2023-09-25" @default.
- W3107084498 title "Photometric Stereo Using CNN-based Feature-Merging Network" @default.
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- W3107084498 doi "https://doi.org/10.23919/iccas50221.2020.9268198" @default.
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