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Deep 3D semantic scene extrapolation

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Abstract

Scene extrapolation is a challenging variant of the scene completion problem, which pertains to predicting the missing part(s) of a scene. While the 3D scene completion algorithms in the literature try to fill the occluded part of a scene such as a chair behind a table, we focus on extrapolating the available half-scene information to a full one, a problem that, to our knowledge, has not been studied yet. Our approaches are based on convolutional neural networks (CNN). As input, we take the half of 3D voxelized scenes, then our models complete the other half of scenes as output. Our baseline CNN model consisting of convolutional and ReLU layers with multiple residual connections and Softmax classifier with voxel-wise cross-entropy loss function at the end. We train and evaluate our models on the synthetic 3D SUNCG dataset. We show that our trained networks can predict the other half of the scenes and complete the objects correctly with suitable lengths. With a discussion on the challenges, we propose scene extrapolation as a challenging test bed for future research in deep learning. We made our models available on https://github.com/aliabbasi/d3dsse.

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Acknowledgements

This work has been supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under the Project EEEAG-215E255. We also thank NVIDIA for their donation of a Titan X graphics card.

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Authors and Affiliations

  1. Middle East Technical University, Ankara, Turkey

    Ali Abbasi, Sinan Kalkan & Yusuf Sahillioğlu

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  1. Ali Abbasi
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  2. Sinan Kalkan
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  3. Yusuf Sahillioğlu
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Correspondence to Ali Abbasi.

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Abbasi, A., Kalkan, S. & Sahillioğlu, Y. Deep 3D semantic scene extrapolation. Vis Comput 35, 271–279 (2019). https://doi.org/10.1007/s00371-018-1586-7

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