Corpus ID: 219573375

Learning to Infer 3D Object Models from Images

@article{Chen2020LearningTI,
  title={Learning to Infer 3D Object Models from Images},
  author={Chang Chen and Fei Deng and Sung-Jin Ahn},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.06130}
}
  • Chang Chen, Fei Deng, Sung-Jin Ahn
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • A crucial ability of human intelligence is to build up models of individual 3D objects from partial scene observations. Recent works have enabled unsupervised 3D representation learning at scene-level, yet learning to decompose the 3D scene into 3D objects and build their individual models from multi-object scene images remains elusive. In this paper, we propose a probabilistic generative model for learning to build modular and compositional 3D object models from observations of a multi-object… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 59 REFERENCES
    Multi-Object Representation Learning with Iterative Variational Inference
    71
    Learning to Exploit Stability for 3D Scene Parsing
    11
    3D Scene Reconstruction With Multi-Layer Depth and Epipolar Transformers
    7
    Geometry-Aware Recurrent Neural Networks for Active Visual Recognition
    9
    Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
    238
    HoloGAN: Unsupervised Learning of 3D Representations From Natural Images
    65
    SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition
    13