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Creators/Authors contains: "A.G. Schwing"

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  1. ; ; ; ; (, IEEE Conference on Computer Vision and Pattern Recognition)
    Accepted Manuscript Full Text Available
  2. ; ; (, International Conference on Machine Learning)
    Reconstructing the 3D shape of objects observed in a single image is a challenging task. Recent approaches rely on visual cues extracted from a given image learned from a deep net. In this work, we leverage recent advances in monocular scene understanding to incorporate an additional geometric cue of surface normals. For this, we proposed a novel optimization layer that encourages the face normals of the reconstructed shape to be aligned with estimated surface normals. We develop a computationally efficient conjugate-gradient-based method that avoids the computation of a high-dimensional sparse matrix. We show this framework to achieve compelling shape reconstruction results on the challenging Pix3D and ShapeNet datasets. 
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    Accepted Manuscript Full Text Available