SIFT is a classical hand-crafted, histogram-based descriptor that has deeply influenced research on image matching for more than a decade. In this paper, a critical review of the aspects that affect SIFT matching performance is carried out, and novel descriptor design strategies are introduced and individually evaluated. These encompass quantization, binarization and hierarchical cascade filtering as means to reduce data storage and increase matching efficiency, with no significant loss of accuracy. An original contextual matching strategy based on a symmetrical variant of the usual nearest-neighbor ratio is discussed as well, that can increase the discriminative power of any descriptor. The paper then undertakes a comprehensive experimental evaluation of state-of-the-art hand-crafted and data-driven descriptors, also including the most recent deep descriptors. Comparisons are carried out according to several performance parameters, among which accuracy and space-time efficiency. Results are provided for both planar and non-planar scenes, the latter being evaluated with a new benchmark based on the concept of approximated patch overlap. Experimental evidence shows that, despite their age, SIFT and other hand-crafted descriptors, once enhanced through the proposed strategies, are ready to meet the future image matching challenges. We also believe that the lessons learned from this work will inspire the design of better hand-crafted and data-driven descriptors.

中文翻译：

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关于 SIFT 匹配有什么新说法吗？

SIFT 是一种经典的手工制作、基于直方图的描述符，十多年来对图像匹配的研究产生了深远的影响。在本文中，对影响 SIFT 匹配性能的方面进行了严格审查，并介绍了新颖的描述符设计策略并对其进行了单独评估。这些包括量化、二值化和分层级联过滤，作为减少数据存储和提高匹配效率的手段，而不会显着降低准确性。还讨论了基于通常最近邻比率的对称变体的原始上下文匹配策略，该策略可以增加任何描述符的判别能力。然后，该论文对最先进的手工制作和数据驱动的描述符进行了全面的实验评估，还包括最新的深度描述符。根据几个性能参数进行比较，其中包括精度和时空效率。为平面和非平面场景提供了结果，后者使用基于近似补丁重叠概念的新基准进行评估。实验证据表明，尽管它们的年龄，SIFT 和其他手工制作的描述符，一旦通过所提出的策略得到增强，就已准备好迎接未来的图像匹配挑战。我们还相信，从这项工作中吸取的经验教训将激发更好的手工和数据驱动描述符的设计。后者使用基于近似补丁重叠概念的新基准进行评估。实验证据表明，尽管它们的年龄，SIFT 和其他手工制作的描述符，一旦通过所提出的策略得到增强，就已准备好迎接未来的图像匹配挑战。我们还相信，从这项工作中吸取的经验教训将激发更好的手工和数据驱动描述符的设计。后者使用基于近似补丁重叠概念的新基准进行评估。实验证据表明，尽管它们的年龄，SIFT 和其他手工制作的描述符，一旦通过所提出的策略得到增强，就已准备好迎接未来的图像匹配挑战。我们还相信，从这项工作中吸取的经验教训将激发更好的手工和数据驱动描述符的设计。