Sparse-to-Dense Hypercolumn Matching for Long-Term Visual Localization
| Autor: | Vincent Lepetit, Guillaume Bourmaud, Hugo Germain |
|---|---|
| Přispěvatelé: | Laboratoire Bordelais de Recherche en Informatique (LaBRI), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université Sciences et Technologies - Bordeaux 1-Université Bordeaux Segalen - Bordeaux 2, Laboratoire de l'intégration, du matériau au système (IMS), Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Bourmaud, Guillaume, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI] 0209 industrial biotechnology Matching (statistics) Computer science Computer Vision and Pattern Recognition (cs.CV) Feature extraction Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Image (mathematics) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 020901 industrial engineering & automation [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 0202 electrical engineering electronic engineering information engineering Image retrieval business.industry [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Pattern recognition Term (time) Visualization Feature (computer vision) Key (cryptography) 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | 3DV 3DV, Sep 2019, Quebec, Canada |
| Popis: | International audience; We propose a novel approach to feature point matching, suitable for robust and accurate outdoor visual localization in long-term scenarios. Given a query image, we first match it against a database of registered reference images, using recent retrieval techniques. This gives us a first estimate of the camera pose. To refine this estimate, like previous approaches, we match 2D points across the query image and the retrieved reference image. This step, however, is prone to fail as it is still very difficult to detect and match sparse feature points across images captured in potentially very different conditions. Our key contribution is to show that we need to extract sparse feature points only in the retrieved reference image: We then search for the corresponding 2D locations in the query image exhaustively. This search can be performed efficiently using convolutional operations , and robustly by using hypercolumn descriptors, i.e. image features computed for retrieval. We refer to this method as 'Sparse-to-Dense Hypercolumn Matching'. Because we know the 3D locations of the sparse feature points in the reference images thanks to an offline reconstruction stage, it is then possible to accurately estimate the camera pose from these matches. Our experiments show that this method allows us to outperform the state-of-the-art on several challenging outdoor datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|