The CUDA LATCH Binary Descriptor: Because Sometimes Faster Means Better
This work addresses the need for faster and efficient feature matching in computer vision applications like structure from motion, though it is incremental as it ports an existing method to a new platform.
The paper tackled the problem of optimizing local image descriptors for speed and quality by presenting a CUDA port of the LATCH binary descriptor to GPU, resulting in high-quality 3D reconstructions at fractions of the time with little loss in quality.
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.