基于深度学习的单视图三维重建

单视图三维重建在计算机视觉领域中是一个具有挑战性的问题. 为了提升现有三维重建算法重建后三维模型的精度, 本文除了提取图像全局特征之外还提取图像局部特征, 结合全局特征和局部特征并选取SDF (signed distance function)作为重建后的三维物体表达方式, 不仅提高了模型的精度, 生成了更高质量的3D形状, 还增强了模型的泛化能力, 使得深度模型可以以较高质量重建出其他物体种类. 实验结果表明, 本文提出的深度网络结构和3D形状表示方法与当今最先进的重建算法相比, 无论在重建后三维模型的效果还是新型物体的泛化中都有更好的表现.

Single-view 3D Reconstruction Based on Deep Learning

Single-view 3D reconstruction is a challenging problem in computer vision. To improve the accuracy of the 3D model reconstructed by the existing 3D reconstruction algorithm, this study extracts both global and local features of the image. On this basis, the signed distance function (SDF) is used to describe the reconstructed 3D objects. In this way, high-quality 3D shapes are generated, and the model has higher accuracy and enhanced generalization capability, which enables the deep model to reconstruct other types of objects with high quality. Experiments demonstrate that compared with the most advanced reconstruction algorithm at present, the proposed deep network and the method for representing 3D shapes have better performance in the effects of reconstructed 3D models and the generalization of new objects.