ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction

TL;DR

ALIKE introduces a differentiable keypoint detection and descriptor extraction method that achieves high accuracy and speed, enabling real-time applications without sacrificing performance.

Contribution

It proposes a partially differentiable detection module with novel loss functions, and a lightweight network for fast, accurate keypoint detection and description.

Findings

Achieves real-time processing at 95 FPS on 640x480 images.

Performs comparably to state-of-the-art methods on homography and pose estimation.

Significantly reduces inference time while maintaining accuracy.

Abstract

Existing methods detect the keypoints in a non-differentiable way, therefore they can not directly optimize the position of keypoints through back-propagation. To address this issue, we present a partially differentiable keypoint detection module, which outputs accurate sub-pixel keypoints. The reprojection loss is then proposed to directly optimize these sub-pixel keypoints, and the dispersity peak loss is presented for accurate keypoints regularization. We also extract the descriptors in a sub-pixel way, and they are trained with the stable neural reprojection error loss. Moreover, a lightweight network is designed for keypoint detection and descriptor extraction, which can run at 95 frames per second for 640x480 images on a commercial GPU. On homography estimation, camera pose estimation, and visual (re-)localization tasks, the proposed method achieves equivalent performance with the state-of-the-art approaches, while greatly reduces the inference time.