Hierarchical Scene Coordinate Classification and Regression for Visual Localization - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

Hierarchical Scene Coordinate Classification and Regression for Visual Localization

Xiaotian Li
  • Fonction : Auteur
Shuzhe Wang
  • Fonction : Auteur
Yi Zhao
  • Fonction : Auteur
Juho Kannala
  • Fonction : Auteur

Résumé

Visual localization is critical to many applications in computer vision and robotics. To address single-image RGB localization, state-of-the-art feature-based methods match local descriptors between a query image and a pre-built 3D model. Recently, deep neural networks have been exploited to regress the mapping between raw pixels and 3D coordinates in the scene, and thus the matching is implicitly performed by the forward pass through the network. However, in a large and ambiguous environment, learning such a regression task directly can be difficult for a single network. In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image. The network consists of a series of output layers with each of them conditioned on the previous ones. The final output layer predicts the 3D coordinates and the others produce progressively finer discrete location labels. The proposed method outperforms the baseline regression-only network and allows us to train single compact models which scale robustly to large environments. It sets a new state-of-the-art for single-image RGB localization performance on the 7-Scenes, 12-Scenes, Cambridge Landmarks datasets, and three combined scenes. Moreover, for large-scale outdoor localization on the Aachen Day-Night dataset, our approach is much more accurate than existing scene coordinate regression approaches, and reduces significantly the performance gap w.r.t. explicit feature matching approaches.
Fichier principal
Vignette du fichier
Li19Hierarchical.pdf (3.7 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02384675 , version 1 (28-11-2019)

Identifiants

Citer

Xiaotian Li, Shuzhe Wang, Yi Zhao, Jakob Verbeek, Juho Kannala. Hierarchical Scene Coordinate Classification and Regression for Visual Localization. CVPR 2020 - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2020, Seattle, United States. pp.11980-11989, ⟨10.1109/CVPR42600.2020.01200⟩. ⟨hal-02384675⟩
423 Consultations
434 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More