论文标题
DKM：密集的内核特征匹配以进行几何估计
DKM: Dense Kernelized Feature Matching for Geometry Estimation
论文作者
论文摘要
功能匹配是一项具有挑战性的计算机视觉任务，涉及在3D场景的两个图像之间找到对应关系。在本文中，我们考虑了密集的方法，而不是更常见的稀疏范式，从而努力找到所有对应关系。也许在违反直觉上，密集的方法先前显示出与稀疏和半帕斯对应物相比的性能，以估计两视频几何形状。这是我们新颖的致密方法的变化，在几何估计上，它的表现优于密集和稀疏方法。新颖性是三倍：首先，我们提出了一个内核回归全球匹配器。其次，我们通过堆叠的特征地图和深度卷积内核提出了经线细化。第三，我们通过一致的深度和平衡的采样方法来提出密集的信心，以进行密集的置信图。通过广泛的实验，我们确认我们提出的密集方法，\ textbf {d} ense \ textbf {k} ernelized feation \ textbf {m} atching在多个几何估计基准上设置了一个新的最新最新时间。特别是，与最佳以前的稀疏方法和密集方法相比，我们对+4.9和+8.9的Megadepth-1500进行了改进。我们的代码在https://github.com/parskatt/dkm上提供
Feature matching is a challenging computer vision task that involves finding correspondences between two images of a 3D scene. In this paper we consider the dense approach instead of the more common sparse paradigm, thus striving to find all correspondences. Perhaps counter-intuitively, dense methods have previously shown inferior performance to their sparse and semi-sparse counterparts for estimation of two-view geometry. This changes with our novel dense method, which outperforms both dense and sparse methods on geometry estimation. The novelty is threefold: First, we propose a kernel regression global matcher. Secondly, we propose warp refinement through stacked feature maps and depthwise convolution kernels. Thirdly, we propose learning dense confidence through consistent depth and a balanced sampling approach for dense confidence maps. Through extensive experiments we confirm that our proposed dense method, \textbf{D}ense \textbf{K}ernelized Feature \textbf{M}atching, sets a new state-of-the-art on multiple geometry estimation benchmarks. In particular, we achieve an improvement on MegaDepth-1500 of +4.9 and +8.9 AUC$@5^{\circ}$ compared to the best previous sparse method and dense method respectively. Our code is provided at https://github.com/Parskatt/dkm
