论文标题
DMIFNET：基于动态多分支信息融合的3D形状重建
DmifNet:3D Shape Reconstruction Based on Dynamic Multi-Branch Information Fusion
论文作者
论文摘要
来自单视图像的3D对象重建是一个长期挑战的问题。以前的工作很难准确地重建3D形状，并具有复杂的拓扑结构，该拓扑在边缘和角落都有丰富的细节。此外，以前的作品使用合成数据来训练其网络，但是在对真实数据进行测试时发生了域的适应问题。在本文中，我们提出了一个动态的多分支信息融合网络（DMIFNET），该网络可以从2D图像中恢复任意拓扑的高保真3D形状。具体而言，我们设计了来自中间层的几个侧支，以使网络产生更多样化的表示形式，以提高网络的概括能力。此外，我们利用狗（高斯差异）从输入图像中提取边缘几何形状和角落信息。然后，我们使用单独的侧分支网络来处理提取的数据，以更好地捕获边缘几何形状和拐角特征信息。最后，我们动态融合了所有分支的信息，以获得最终的预测概率。大规模公开可用数据集的广泛定性和定量实验证明了我们方法的有效性和效率。代码和模型可在https://github.com/leilimaster/dmifnet上公开获取。
3D object reconstruction from a single-view image is a long-standing challenging problem. Previous work was difficult to accurately reconstruct 3D shapes with a complex topology which has rich details at the edges and corners. Moreover, previous works used synthetic data to train their network, but domain adaptation problems occurred when tested on real data. In this paper, we propose a Dynamic Multi-branch Information Fusion Network (DmifNet) which can recover a high-fidelity 3D shape of arbitrary topology from a 2D image. Specifically, we design several side branches from the intermediate layers to make the network produce more diverse representations to improve the generalization ability of network. In addition, we utilize DoG (Difference of Gaussians) to extract edge geometry and corners information from input images. Then, we use a separate side branch network to process the extracted data to better capture edge geometry and corners feature information. Finally, we dynamically fuse the information of all branches to gain final predicted probability. Extensive qualitative and quantitative experiments on a large-scale publicly available dataset demonstrate the validity and efficiency of our method. Code and models are publicly available at https://github.com/leilimaster/DmifNet.
