This work focuses on mitigating two limitations in the joint learning of
local feature detectors and descriptors. First, the ability to estimate the
local shape (scale, orientation, etc.) of feature points is often neglected
during dense feature extraction, while the shape-awareness is crucial to
acquire stronger geometric invariance. Second, the localization accuracy of
detected keypoints is not sufficient to reliably recover camera geometry, which
has become the bottleneck in tasks such as 3D reconstruction. In this paper, we
present ASLFeat, with three light-weight yet effective modifications to
mitigate above issues. First, we resort to deformable convolutional networks to
densely estimate and apply local transformation. Second, we take advantage of
the inherent feature hierarchy to restore spatial resolution and low-level
details for accurate keypoint localization. Finally, we use a peakiness
measurement to relate feature responses and derive more indicative detection
scores. The effect of each modification is thoroughly studied, and the
evaluation is extensively conducted across a variety of practical scenarios.
State-of-the-art results are reported that demonstrate the superiority of our
methods.

ASLFeat 是一种新的本地特征检测器和描述符结合的方法，使用可变形卷积网络估计本地特征点的形状，从而实现更强的几何不变性。此外，ASLFeat 使用层次结构以恢复空间分辨率和局部细节，并通过尖锐度测量来推导最有指示性的检测分数，以进一步提高检测精度和可靠性，并可在各种实际场景中获得最新的研究成果。

ASLFeat: 学习准确形状和定位的本地特征

ASLFeat: Learning Local Features of Accurate Shape and Localization

We present a large scale benchmark for the evaluation of local feature
detectors. Our key innovation is the introduction of a new evaluation protocol
which extends and improves the standard detection repeatability measure. The
new protocol is better for assessment on a large number of images and reduces
the dependency of the results on unwanted distractors such as the number of
detected features and the feature magnification factor. Additionally, our
protocol provides a comprehensive assessment of the expected performance of
detectors under several practical scenarios. Using images from the
recently-introduced HPatches dataset, we evaluate a range of state-of-the-art
local feature detectors on two main tasks: viewpoint and illumination invariant
detection. Contrary to previous detector evaluations, our study contains an
order of magnitude more image sequences, resulting in a quantitative evaluation
significantly more robust to over-fitting. We also show that traditional
detectors are still very competitive when compared to recent deep-learning
alternatives.

该研究提出了一种新的评估协议，可对大量图像上的本地特征检测器进行综合评估，使用 HPatches 数据集评估了许多本地特征检测器的视角和光照不变性检测，与以往相比，该研究的图像数量更多，结果更具鲁棒性，同时也表明传统检测器在与最近的深度学习替代方案相比时仍然具有很高的竞争力。