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.
CITATION STYLE
Cao, Y. P., Liu, Z. N., Kuang, Z. F., Kobbelt, L., & Hu, S. M. (2018). Learning to reconstruct high-quality 3D shapes with cascaded fully convolutional networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11213 LNCS, pp. 626–643). Springer Verlag. https://doi.org/10.1007/978-3-030-01240-3_38
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