SuperPoint: Self-Supervised Interest Point Detection and Description
| Autor: | Tomasz Malisiewicz, Daniel DeTone, Andrew Rabinovich |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Boosting (machine learning) Artificial neural network Contextual image classification business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition Scale-invariant feature transform 02 engineering and technology Image segmentation Object detection Interest point detection 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Homography 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | CVPR Workshops |
| Popis: | This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB. Camera-ready version for CVPR 2018 Deep Learning for Visual SLAM Workshop (DL4VSLAM2018) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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