SEKD: Self-Evolving Keypoint Detection and Description

TL;DR

SEKD introduces a self-supervised framework for local feature detection and description that emphasizes repeatability and reliability, outperforming existing methods in various vision tasks.

Contribution

The paper proposes a novel self-evolving, self-supervised approach that jointly optimizes local feature detector and descriptor properties, with new training strategies and comprehensive benchmarking.

Findings

Outperforms popular hand-crafted and DNN-based methods

Achieves significant improvements in homography and pose estimation

Validated through extensive ablation studies

Abstract

Researchers have attempted utilizing deep neural network (DNN) to learn novel local features from images inspired by its recent successes on a variety of vision tasks. However, existing DNN-based algorithms have not achieved such remarkable progress that could be partly attributed to insufficient utilization of the interactive characters between local feature detector and descriptor. To alleviate these difficulties, we emphasize two desired properties, i.e., repeatability and reliability, to simultaneously summarize the inherent and interactive characters of local feature detector and descriptor. Guided by these properties, a self-supervised framework, namely self-evolving keypoint detection and description (SEKD), is proposed to learn an advanced local feature model from unlabeled natural images. Additionally, to have performance guarantees, novel training strategies have also been dedicatedly designed to minimize the gap between the learned feature and its properties. We benchmark the proposed method on homography estimation, relative pose estimation, and structure-from-motion tasks. Extensive experimental results demonstrate that the proposed method outperforms popular hand-crafted and DNN-based methods by remarkable margins. Ablation studies also verify the effectiveness of each critical training strategy. We will release our code along with the trained model publicly.