SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D Scans

Angela Dai, Christian Diller, Matthias Niessner; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 849-858

Abstract


We present a novel approach that converts partial and noisy RGB-D scans into high-quality 3D scene reconstructions by inferring unobserved scene geometry. Our approach is fully self-supervised and can hence be trained solely on incomplete, real-world scans. To achieve, self-supervision, we remove frames from a given (incomplete) 3D scan in order to make it even more incomplete; self-supervision is then formulated by correlating the two levels of partialness of the same scan while masking out regions that have never been observed. Through generalization across a large training set, we can then predict 3D scene completions even without seeing any 3D scan of entirely complete geometry. Combined with a new 3D sparse generative convolutional neural network architecture, our method is able to predict highly detailed surfaces in a coarse-to-fine hierarchical fashion that outperform existing state-of-the-art methods by a significant margin in terms of reconstruction quality.

Related Material


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[bibtex]
@InProceedings{Dai_2020_CVPR,
author = {Dai, Angela and Diller, Christian and Niessner, Matthias},
title = {SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D Scans},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}