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• W2953808575 abstract "The growing availability of commodity RGB-D cameras has boosted the applications in the field of scene understanding. However, as a fundamental scene understanding task, surface normal estimation from RGB-D data lacks thorough investigation. In this paper, a hierarchical fusion network with adaptive feature re-weighting is proposed for surface normal estimation from a single RGB-D image. Specifically, the features from color image and depth are successively integrated at multiple scales to ensure global surface smoothness while preserving visually salient details. Meanwhile, the depth features are re-weighted with a confidence map estimated from depth before merging into the color branch to avoid artifacts caused by input depth corruption. Additionally, a hybrid multi-scale loss function is designed to learn accurate normal estimation given noisy ground-truth dataset. Extensive experimental results validate the effectiveness of the fusion strategy and the loss design, outperforming state-of-the-art normal estimation schemes." @default.
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• W2953808575 date "2019-04-06" @default.
• W2953808575 modified "2023-09-23" @default.
• W2953808575 title "Deep Surface Normal Estimation with Hierarchical RGB-D Fusion" @default.
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• W2953808575 doi "https://doi.org/10.48550/arxiv.1904.03405" @default.
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