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• W2895524927 abstract "We present a data-driven approach to reconstructing high-resolution and detailed volumetric representations of 3D shapes. Although well studied, algorithms for volumetric fusion from multi-view depth scans are still prone to scanning noise and occlusions, making it hard to obtain high-fidelity 3D reconstructions. In this paper, inspired by recent advances in efficient 3D deep learning techniques, we introduce a novel cascaded 3D convolutional network architecture, which learns to reconstruct implicit surface representations from noisy and incomplete depth maps in a progressive, coarse-to-fine manner. To this end, we also develop an algorithm for end-to-end training of the proposed cascaded structure. Qualitative and quantitative experimental results on both simulated and real-world datasets demonstrate that the presented approach outperforms existing state-of-the-art work in terms of quality and fidelity of reconstructed models." @default.
• W2895524927 created "2018-10-12" @default.
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• W2895524927 date "2018-01-01" @default.
• W2895524927 modified "2023-10-01" @default.
• W2895524927 title "Learning to Reconstruct High-Quality 3D Shapes with Cascaded Fully Convolutional Networks" @default.
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• W2895524927 doi "https://doi.org/10.1007/978-3-030-01240-3_38" @default.
• W2895524927 hasPublicationYear "2018" @default.
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