论文标题
端到端的多视图结构从具有超相关量
End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volumes
论文作者
论文摘要
基于图像的3D重建是计算机视觉中最重要的任务之一，在过去的几十年中提出了许多解决方案。目的是从图像直接提取场景对象的几何形状。然后，这些可以用于多种应用中，例如电影，游戏，虚拟现实等。最近，已经提出了深度学习技术来解决这个问题。他们依靠对大量数据进行培训，以通过深层卷积神经网络在图像之间进行关联，并已被证明超过了传统的程序技术。在本文中，我们通过合并4D相关量来改进[11]的最先进的两视频结构（SFM）方法，以进行更准确的特征匹配和重建。此外，我们将其扩展到一般的多视图案例，并在复杂的基准数据集DTU [4]上对其进行评估。定量评估和与最先进的多视图3D重建方法的比较证明了其在重建的准确性方面的优势。
Image-based 3D reconstruction is one of the most important tasks in Computer Vision with many solutions proposed over the last few decades. The objective is to extract metric information i.e. the geometry of scene objects directly from images. These can then be used in a wide range of applications such as film, games, virtual reality, etc. Recently, deep learning techniques have been proposed to tackle this problem. They rely on training on vast amounts of data to learn to associate features between images through deep convolutional neural networks and have been shown to outperform traditional procedural techniques. In this paper, we improve on the state-of-the-art two-view structure-from-motion(SfM) approach of [11] by incorporating 4D correlation volume for more accurate feature matching and reconstruction. Furthermore, we extend it to the general multi-view case and evaluate it on the complex benchmark dataset DTU [4]. Quantitative evaluations and comparisons with state-of-the-art multi-view 3D reconstruction methods demonstrate its superiority in terms of the accuracy of reconstructions.
