GEOMETRIC CAMERA POSE REFINEMENT WITH LEARNED DEPTH MAPS
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | GEOMETRIC CAMERA POSE REFINEMENT WITH LEARNED DEPTH MAPS |
| Type de publication | Conference Paper |
| Year of Publication | 2019 |
| Auteurs | Piasco N, Sidibe D, Demonceaux C, Gouet-Brunet V |
| Conference Name | 2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
| Publisher | Inst Elect & Elect Engineers; Inst Elect & Elect Engineers Signal Proc Soc |
| Conference Location | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| ISBN Number | 978-1-5386-6249-6 |
| Mots-clés | Camera relocalisation, depth from monocular, ICP, multimodal data, pose estimation |
| Résumé | We present a new method for image-only camera relocalisation composed of a fast image indexing retrieval step followed by pose refinement based on ICP (Iterative Closest Point). The first step aims to find an initial pose for the query by evaluating images similarity with low dimensional global deep descriptors. Subsequently, we predict with a fully convolutional deep encoder-decoder neural network a dense depth map from the image query. We use this depth map to create a local point cloud and refine the initial query pose using an ICP algorithm. We demonstrate the effectiveness of our new approach on various indoor scenes. Compared to learned pose regression methods, our proposal can be used on multiple scenes without the need of a specific weights-setup for each scene, while showing equivalent results. |