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 this https URL The
supplementary can be found at
this https URL

本文提出了一种新颖的用于单视角图像重构三维模型的方法 ——Deep Implicit Surface Network (DISN), 结合全局和局部图像特征以显著提高预测精度，能够捕捉 3D 形状中的细节，包括孔洞和薄结构等，并且在多类形状的合成与实际图像重构中达到了最优性能。