Visual localization is a key capability for embodied agents, but deploying state-of-the-art deep feature matchers on edge devices is hampered by their massive computational cost. The paper introduces LateMatch, a method designed to address this limitation by making the matching process more efficient.
The authors point out that standard deep matchers tightly interleave computation, which creates an interaction bottleneck. LateMatch breaks this bottleneck through a cacheable matching strategy, allowing parts of the computation to be reused rather than repeated. This is presented as a way to reduce the computational burden without abandoning deep feature matching.
The significance lies in the potential to run advanced visual localization on resource-constrained devices, which is important for robotics and other embodied applications. However, the abstract does not include specific performance metrics or comparisons, so the actual gains remain unquantified in the available text.