  Reconstructing 3D shapes from single-view images has been a long-standing
research problem. In this paper, we present DISN, a Deep Implicit Surface
Network which can generate a high-quality detail-rich 3D mesh from an 2D image
by predicting the underlying signed distance fields. In addition to utilizing
global image features, DISN predicts the projected location for each 3D point
on the 2D image, and extracts local features from the image feature maps.
Combining global and local features significantly improves the accuracy of the
signed distance field prediction, especially for the detail-rich areas. To the
best of our knowledge, DISN is the first method that constantly captures
details such as holes and thin structures present in 3D shapes from single-view
images. DISN achieves the state-of-the-art single-view reconstruction
performance on a variety of shape categories reconstructed from both synthetic
and real images. Code is available at https://github.com/xharlie/DISN The
supplementary can be found at
https://xharlie.github.io/images/neurips_2019_supp.pdf
