We propose a new dataset for learning local image descriptors which can be
used for significantly improved patch matching. Our proposed dataset consists
of an order of magnitude more number of scenes, images, and positive and
negative correspondences compared to the currently available Multi-View Stereo
(MVS) dataset from Brown et al. The new dataset also has better coverage of the
overall viewpoint, scale, and lighting changes in comparison to the MVS
dataset. Our dataset also provides supplementary information like RGB patches
with scale and rotations values, and intrinsic and extrinsic camera parameters
which as shown later can be used to customize training data as per application.
We train an existing state-of-the-art model on our dataset and evaluate on
publicly available benchmarks such as HPatches dataset and Strecha et
al.\cite{strecha} to quantify the image descriptor performance. Experimental
evaluations show that the descriptors trained using our proposed dataset
outperform the current state-of-the-art descriptors trained on MVS by 8%, 4%
and 10% on matching, verification and retrieval tasks respectively on the
HPatches dataset. Similarly on the Strecha dataset, we see an improvement of
3-5% for the matching task in non-planar scenes.

我们提出了一个新的数据集，用于学习本地图像描述符，可用于显着改善补丁匹配。此新数据集的训练效果比目前的 Multi-View Stereo (MVS) 数据集更好，并且还提供了补充信息，例如 RGB 补丁、相机参数等。我们使用此数据集训练现有的最先进模型，并在公开基准测试中获得了比 MVS 数据集训练的最先进描述符更好的性能。

一个用于改善补丁匹配的大型数据集

A Large Dataset for Improving Patch Matching

We propose a convolutional neural network (ConvNet) based approach for
learning local image descriptors which can be used for significantly improved
patch matching and 3D reconstructions. A multi-resolution ConvNet is used for
learning keypoint descriptors. We also propose a new dataset consisting of an
order of magnitude more number of scenes, images, and positive and negative
correspondences compared to the currently available Multi-View Stereo (MVS)
[18] dataset. The new dataset also has better coverage of the overall
viewpoint, scale, and lighting changes in comparison to the MVS dataset. We
evaluate our approach on publicly available datasets, such as Oxford Affine
Covariant Regions Dataset (ACRD) [12], MVS [18], Synthetic [6] and Strecha [15]
datasets to quantify the image descriptor performance. Scenes from the Oxford
ACRD, MVS and Synthetic datasets are used for evaluating the patch matching
performance of the learnt descriptors while the Strecha dataset is used to
evaluate the 3D reconstruction task. Experiments show that the proposed
descriptor outperforms the current state-of-the-art descriptors in both the
evaluation tasks.

提出了一种基于卷积神经网络 (ConvNet) 的方法，用于学习本地图像描述符，可用于显着改善补丁匹配和三维重建，该方法使用多分辨率 ConvNets 学习关键点描述符，并且提出了一个新的数据集，包含比当前可用的 Multi-View Stereo（MVS）数据集更多的图像和正负对应物，并且在视角，尺度和光照变化方面具有更好的覆盖范围。该文对基于提出的描述符学习的补丁匹配和三维重建任务进行了评估，实验结果表明，在评价任务中，提出的描述符优于当前现有的最先进描述符。