Image keypoints and descriptors play a crucial role in many visual measurement tasks. In recent years, deep neural networks have been widely used to improve the performance of keypoint and descriptor extraction. However, the conventional convolution operations do not provide the geometric invariance required for the descriptor. To address this issue, we propose the Sparse Deformable Descriptor Head (SDDH), which learns the deformable positions of supporting features for each keypoint and constructs deformable descriptors. Furthermore, SDDH extracts descriptors at sparse keypoints instead of a dense descriptor map, which enables efficient extraction of descriptors with strong expressiveness. In addition, we relax the neural reprojection error (NRE) loss from dense to sparse to train the extracted sparse descriptors. Experimental results show that the proposed network is both efficient and powerful in various visual measurement tasks, including image matching, 3D reconstruction, and visual relocalization.

翻译：图像关键点与描述子在许多视觉测量任务中发挥着关键作用。近年来，深度神经网络被广泛用于提升关键点与描述子提取的性能。然而，传统的卷积运算无法提供描述子所需的几何不变性。为解决这一问题，我们提出稀疏可变形描述子头（SDDH），该方法能够学习每个关键点支撑特征的可变形位置，并构建可变形描述子。此外，SDDH在稀疏关键点上提取描述子，而非生成稠密的描述子图，从而能够高效提取具有强表达能力的描述子。同时，我们将神经重投影误差（NRE）损失从稠密形式松弛为稀疏形式，以训练所提取的稀疏描述子。实验结果表明，该网络在图像匹配、三维重建和视觉重定位等多种视觉测量任务中既高效又强大。