Toward 3D Object Reconstruction from Stereo Images Haozhe Xie, Hongxun Yao, Shangchen Zhou, Shengping Zhang, Xiaojun Tong, Wenxiu Sun
Abstract Inferring the complete 3D shape of an object from an RGB image has shown impressive results, however, existing methods rely primarily on recognizing the most similar 3D model from the training set to solve the problem. These methods suffer from poor generalization and may lead to low-quality reconstructions for unseen objects.
Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images Haozhe Xie, Hongxun Yao, Xiaoshuai Sun, Shangchen Zhou, Shengping Zhang, Wenxiu Sun
Abstract Recovering the 3D representation of an object from single-view or multi-view RGB images by deep neural networks has attracted increasing attention in the past few years. Several mainstream works (e.g., 3D-R2N2) use recurrent neural networks (RNNs) to fuse multiple feature maps extracted from input images sequentially. However, when given the same set of input images with different orders, RNN-based approaches are unable to produce consistent reconstruction results.
Weighted Voxel: a novel voxel representation for 3D reconstruction Haozhe Xie, Hongxun Yao, Xiaoshuai Sun, Shangchen Zhou, Xiaojun Tong
Abstract 3D reconstruction has been attracting increasing attention in the past few years. With the surge of deep neural networks, the performance of 3D reconstruction has been improved significantly. However, the voxel reconstructed by extant approaches usually contains lots of noise and leads to heavy computation. In this paper, we define a new voxel representation, named Weighted Voxel.