论文标题
多视图引导的多视图立体声
Multi-View Guided Multi-View Stereo
论文作者
论文摘要
本文引入了一个新的深层框架，用于从多个图像框架中重建密集的3D重建，利用一组稀疏的深度测量值共同收集了图像采集。鉴于深度多视图立体网络，我们的框架使用稀疏的深度提示来指导神经网络，通过调节向前步骤中建立的飞机扫描成本量，使我们能够不断推断更准确的深度图。此外，由于多个观点可以提供额外的深度测量值，因此我们提出了一种多视图指导策略，该策略增加了用于指导网络的稀疏点的密度，从而导致更准确的结果。我们在各种最先进的深度视角立体网络中评估了我们的多视图指导框架，这证明了它在改善每个人在BlendenDMVG和DTU数据集中取得的结果方面的有效性。
This paper introduces a novel deep framework for dense 3D reconstruction from multiple image frames, leveraging a sparse set of depth measurements gathered jointly with image acquisition. Given a deep multi-view stereo network, our framework uses sparse depth hints to guide the neural network by modulating the plane-sweep cost volume built during the forward step, enabling us to infer constantly much more accurate depth maps. Moreover, since multiple viewpoints can provide additional depth measurements, we propose a multi-view guidance strategy that increases the density of the sparse points used to guide the network, thus leading to even more accurate results. We evaluate our Multi-View Guided framework within a variety of state-of-the-art deep multi-view stereo networks, demonstrating its effectiveness at improving the results achieved by each of them on BlendedMVG and DTU datasets.
