Papers › Rethinking Low-level Features for Interest Point Detection and Description
Rethinking Low-level Features for Interest Point Detection and Description
Changhao Wang, Guanwen Zhang, Zhengyun Cheng, and Wei Zhou
Although great efforts have been made for interest point detection and description, the current learning-based methods that use high-level features from the higher layers of Convolutional Neural Networks (CNN) do not completely outperform the conventional methods. On the one hand, interest points are semantically ill-defined and high-level features that emphasize semantic information are not adequate to describe interest points; On the other hand, the existing methods using low-level information usually perform detection on multi-level feature maps, which is time consuming for real time applications. To address these problems, we propose a Low-level descriptor-Aware Network (LANet) for interest point detection and description in self-supervised learning. Specifically, the proposed LANet exploits the low-level features for interest point description while using high-level features for interest point detection. Experimental results demonstrate that LANet achieves state-of-the-art performance on the homography estimation benchmark. Notably, the proposed LANet is a front-end feature learning framework that can be deployed in downstream tasks that require interest points with high-quality descriptors.
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