We propose a novel approach to feature point matching, suitable for robust
and accurate outdoor visual localization in long-term scenarios. Given a query
image, we first match it against a database of registered reference images,
using recent retrieval techniques. This gives us a first estimate of the camera
pose. To refine this estimate, like previous approaches, we match 2D points
across the query image and the retrieved reference image. This step, however,
is prone to fail as it is still very difficult to detect and match sparse
feature points across images captured in potentially very different conditions.
Our key contribution is to show that we need to extract sparse feature points
only in the retrieved reference image: We then search for the corresponding 2D
locations in the query image exhaustively. This search can be performed
efficiently using convolutional operations, and robustly by using hypercolumn
descriptors, i.e. image features computed for retrieval. We refer to this
method as Sparse-to-Dense Hypercolumn Matching. Because we know the 3D
locations of the sparse feature points in the reference images thanks to an
offline reconstruction stage, it is then possible to accurately estimate the
camera pose from these matches. Our experiments show that this method allows us
to outperform the state-of-the-art on several challenging outdoor datasets.

我们提出了一种新颖的功能点匹配方法，适用于长期场景下的在户外进行稳健准确的视觉定位。我们在已注册的参考图像数据库中，使用最近的检索技术将查询图像进行匹配，通过这个得出第一个相机位姿的估计。然后，我们在查询图像和检索出来的参考图像之间匹配 2D 特征点以优化位姿估计。与以前的方法不同的是，我们只需在检索出来的参考图像中稀疏提取特征点，然后用 Sparse-to-Dense Hypercolumn Matching 方法进行快速、稳健的匹配，最终估计相机位姿。我们的实验表明，这种方法在多个具有挑战性的户外数据集上表现优于现有技术。