See the Glass Half Full: Reasoning About Liquid Containers, Their Volume and Content

@article{Mottaghi2017SeeTG,
  title={See the Glass Half Full: Reasoning About Liquid Containers, Their Volume and Content},
  author={R. Mottaghi and C. Schenck and D. Fox and Ali Farhadi},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={1889-1898}
}
  • R. Mottaghi, C. Schenck, +1 author Ali Farhadi
  • Published 2017
  • Computer Science
  • 2017 IEEE International Conference on Computer Vision (ICCV)
  • Humans have rich understanding of liquid containers and their contents; for example, we can effortlessly pour water from a pitcher to a cup. Doing so requires estimating the volume of the cup, approximating the amount of water in the pitcher, and predicting the behavior of water when we tilt the pitcher. Very little attention in computer vision has been made to liquids and their containers. In this paper, we study liquid containers and their contents, and propose methods to estimate the volume… CONTINUE READING
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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 53 REFERENCES
    Microsoft COCO: Common Objects in Context
    • 10,292
    • PDF
    Deep Residual Learning for Image Recognition
    • 50,397
    • Highly Influential
    • PDF
    Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
    • 18,452
    • PDF
    Fully Convolutional Networks for Semantic Segmentation
    • 7,201
    • PDF
    Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
    • 2,440
    • PDF
    Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
    • 1,576
    • PDF
    Single image depth estimation from predicted semantic labels
    • 377
    • PDF
    "What Happens If..." Learning to Predict the Effect of Forces in Images
    • 81
    • PDF
    Putting Objects in Perspective
    • 502
    • PDF
    Fill and Transfer: A Simple Physics-Based Approach for Containability Reasoning
    • 19
    • PDF