3D shape reconstruction from a single-view RGB image is an ill-posed problem due to the invisible parts of the object to be reconstructed. Most of the existing methods rely on large-scale data to obtain shape priors through tuning parameters of reconstruction models. These methods might not be able to deal with the cases with heavy object occlusions and noisy background since prior information can not be retained completely or applied efficiently. In this paper, we are the first to develop a memory-based meta-learning framework for single-view 3D reconstruction. A write controller is designed to extract shape-discriminative features from images and store image features and their corresponding volumes into external memory. A read controller is proposed to sequentially encode shape priors related to the input image and predict a shape-specific refiner. Experimental results demonstrate that our Meta3D outperforms state-of-the-art methods with a large margin through retaining shape priors explicitly, especially for the extremely difficult cases.

本文提出一种名为Mem3D的新方法，通过构建形状先验和使用基于LSTM的形状编码器，以及使用Voxel Triplet Loss函数来重建包含噪声和遮挡的3D物体形状，该方法在ShapeNet和Pix3D数据集上表现优异。

基于形状先验的单视角三维物体重建