Visual Localization via Few-Shot Scene Region Classification
This addresses the inefficiency in visual localization for computer vision and robotics applications, offering a more practical solution with reduced data requirements.
The paper tackles the problem of visual localization requiring large amounts of training data by proposing a scene region classification approach that achieves fast and effective scene memorization with few-shot images, reducing training time to only a few minutes.
Visual (re)localization addresses the problem of estimating the 6-DoF (Degree of Freedom) camera pose of a query image captured in a known scene, which is a key building block of many computer vision and robotics applications. Recent advances in structure-based localization solve this problem by memorizing the mapping from image pixels to scene coordinates with neural networks to build 2D-3D correspondences for camera pose optimization. However, such memorization requires training by amounts of posed images in each scene, which is heavy and inefficient. On the contrary, few-shot images are usually sufficient to cover the main regions of a scene for a human operator to perform visual localization. In this paper, we propose a scene region classification approach to achieve fast and effective scene memorization with few-shot images. Our insight is leveraging a) pre-learned feature extractor, b) scene region classifier, and c) meta-learning strategy to accelerate training while mitigating overfitting. We evaluate our method on both indoor and outdoor benchmarks. The experiments validate the effectiveness of our method in the few-shot setting, and the training time is significantly reduced to only a few minutes. Code available at: \url{https://github.com/siyandong/SRC}