Keypoint detection is the foundation of many computer vision tasks, including image registration, structure-from-motion, 3D reconstruction, visual odometry, and SLAM. Traditional detectors (SIFT, ORB, BRISK, FAST, etc.) and learning-based methods (SuperPoint, R2D2, QuadNet, LIFT, etc.) have shown strong performance gains yet suffer from key limitations: sensitivity to photometric changes, low keypoint density and repeatability, limited adaptability to challenging scenes, and lack of semantic understanding, often failing to prioritize visually important regions. We present DeepDetect, an intelligent, all-in-one, dense detector that unifies the strengths of classical detectors using deep learning. Firstly, we create ground-truth masks by fusing outputs of 7 keypoint and 2 edge detectors, extracting diverse visual cues from corners and blobs to prominent edges and textures in the images. Afterwards, a lightweight and efficient model: ESPNet, is trained using fused masks as labels, enabling DeepDetect to focus semantically on images while producing highly dense keypoints, that are adaptable to diverse and visually degraded conditions. Evaluations on Oxford, HPatches, and Middlebury datasets demonstrate that DeepDetect surpasses other detectors achieving maximum values of 0.5143 (average keypoint density), 0.9582 (average repeatability), 338,118 (correct matches), and 842,045 (voxels in stereo 3D reconstruction).

翻译：关键点检测是许多计算机视觉任务的基础，包括图像配准、运动恢复结构、三维重建、视觉里程计和SLAM。传统检测器（SIFT、ORB、BRISK、FAST等）与基于学习的方法（SuperPoint、R2D2、QuadNet、LIFT等）虽已展现出显著的性能提升，但仍存在关键局限：对光度变化敏感、关键点密度与可重复性低、对挑战性场景的适应能力有限，以及缺乏语义理解，往往未能优先关注视觉上重要的区域。我们提出了DeepDetect，一种智能、一体化、密集的检测器，它利用深度学习统一了经典检测器的优势。首先，我们通过融合7种关键点检测器和2种边缘检测器的输出来创建真实掩码，从图像中的角点、斑点提取到显著边缘和纹理的多样化视觉线索。随后，使用融合掩码作为标签训练一个轻量高效的模型：ESPNet，使DeepDetect能够从语义上聚焦图像，同时生成高度密集的关键点，并能适应多样化和视觉退化条件。在Oxford、HPatches和Middlebury数据集上的评估表明，DeepDetect超越了其他检测器，取得了0.5143（平均关键点密度）、0.9582（平均可重复性）、338,118（正确匹配数）和842,045（立体三维重建体素数）的最大值。