Accuracy, descriptor size, and the time required for extraction and matching are all important factors when selecting local image descriptors. To optimize over all these requirements, this paper presents a CUDA port for the recent Learned Arrangement of Three Patches (LATCH) binary descriptors to the GPU platform. The design of LATCH makes it well suited for GPU processing. Owing to its small size and binary nature, the GPU can further be used to efficiently match LATCH features. Taken together, this leads to breakneck descriptor extraction and matching speeds. We evaluate the trade off between these speeds and the quality of results in a feature matching intensive application. To this end, we use our proposed CUDA LATCH (CLATCH) to recover structure from motion (SfM), comparing 3D reconstructions and speed using different representations. Our results show that CLATCH provides high quality 3D reconstructions at fractions of the time required by other representations, with little, if any, loss of reconstruction quality.

本文介绍了基于CUDA平台的Learned Arrangement of Three Patches（LATCH）二进制描述符，LATCH的设计精良适合于GPU处理。由于其小尺寸和二进制特性，GPU可用于高效匹配LATCH特征导致描述符提取和匹配速度非常快。我们使用CUDA LATCH（CLATCH）从运动中恢复结构，将3D重建和速度与不同表示进行比较，结果表明，CLATCH在仅需其他表示所需时间的几分之一的时间内提供高质量的3D重建，几乎没有损失重建质量。

CUDA LATCH 二进制描述符：有时候更快意味着更好