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Image-to-Voxel Model Translation for 3D Scene Reconstruction and Segmentation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12352))

Abstract

Objects class, depth, and shape are instantly reconstructed by a human looking at a 2D image. While modern deep models solve each of these challenging tasks separately, they struggle to perform simultaneous scene 3D reconstruction and segmentation. We propose a single shot image-to-semantic voxel model translation framework. We train a generator adversarially against a discriminator that verifies the object’s poses. Furthermore, trapezium-shaped voxels, volumetric residual blocks, and 2D-to-3D skip connections facilitate our model learning explicit reasoning about 3D scene structure. We collected a SemanticVoxels dataset with 116k images, ground-truth semantic voxel models, depth maps, and 6D object poses. Experiments on ShapeNet and our SemanticVoxels datasets demonstrate that our framework achieves and surpasses state-of-the-art in the reconstruction of scenes with multiple non-rigid objects of different classes. We made our model and dataset publicly available (http://www.zefirus.org/SSZ).

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Acknowledgments

The reported study was funded by Russian Foundation for Basic Research (RFBR) according to the research project N\(\mathrm {^{o}}\) 17-29-04509.

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Correspondence to Vladimir A. Knyaz .

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Kniaz, V.V., Knyaz, V.A., Remondino, F., Bordodymov, A., Moshkantsev, P. (2020). Image-to-Voxel Model Translation for 3D Scene Reconstruction and Segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12352. Springer, Cham. https://doi.org/10.1007/978-3-030-58571-6_7

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