What Do Single-View 3D Reconstruction Networks Learn?

Convolutional networks for single-view object reconstruction have shown impressive performance and have become a popular subject of research. All existing techniques are united by the idea of having an encoder-decoder network that performs non-trivial reasoning about the 3D structure of the output s...

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Veröffentlicht in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 3400 - 3409
Hauptverfasser: Tatarchenko, Maxim, Richter, Stephan R., Ranftl, Rene, Li, Zhuwen, Koltun, Vladlen, Brox, Thomas
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2019
Schlagworte:
3D from Single Image
Cognition
Computer vision
Convolutional neural networks
Coordinate measuring machines
Deep Learning
Image classification
Image reconstruction
Pattern recognition
Three-dimensional displays
ISSN:1063-6919
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Zusammenfassung:Convolutional networks for single-view object reconstruction have shown impressive performance and have become a popular subject of research. All existing techniques are united by the idea of having an encoder-decoder network that performs non-trivial reasoning about the 3D structure of the output space. In this work, we set up two alternative approaches that perform image classification and retrieval respectively. These simple baselines yield better results than state-of-the-art methods, both qualitatively and quantitatively. We show that encoder-decoder methods are statistically indistinguishable from these baselines, thus indicating that the current state of the art in single-view object reconstruction does not actually perform reconstruction but image classification. We identify aspects of popular experimental procedures that elicit this behavior and discuss ways to improve the current state of research.
ISSN:1063-6919
DOI:10.1109/CVPR.2019.00352