Learning to Reconstruct High-quality 3D Shapes with Cascaded Fully Convolutional Networks

Yan-Pei Cao, Zheng-Ning Liu, Zheng-Fei Kuang, Leif Kobbelt, Shi-Min Hu; The European Conference on Computer Vision (ECCV), 2018, pp. 616-633

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.

Related Material


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[bibtex]
@InProceedings{Cao_2018_ECCV,
author = {Cao, Yan-Pei and Liu, Zheng-Ning and Kuang, Zheng-Fei and Kobbelt, Leif and Hu, Shi-Min},
title = {Learning to Reconstruct High-quality 3D Shapes with Cascaded Fully Convolutional Networks},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}