ALIKED：通过可变形变换实现的更轻量关键点与描述符提取网络
ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation
AI总结：
本文提出 ALIKED 网络，通过稀疏可变形描述符头（SDDH）和稀疏化 NRE 损失，在关键点处高效提取具有几何不变性的可变形描述符，在图像匹配、三维重建和视觉重定位等任务中兼顾效率与性能。
摘要：
图像关键点和描述符在许多视觉测量任务中起着至关重要的作用。近年来，深度神经网络被广泛用于提升关键点和描述符提取的性能。然而，传统卷积操作无法提供描述符所需的几何不变性。为了解决这一问题，我们提出了稀疏可变形描述符头（SDDH），它为每个关键点学习支持特征的可变形位置，并构建可变形描述符。此外，SDDH 在稀疏关键点处提取描述符，而不是生成稠密描述符图，从而能够高效地提取具有强表达能力的描述符。另外，我们将神经重投影误差（NRE）损失从稠密形式放宽为稀疏形式，以训练提取出的稀疏描述符。实验结果表明，所提出的网络在多种视觉测量任务中既高效又强大，包括图像匹配、三维重建和视觉重定位。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
