TBPos: Dataset for Large-Scale Precision Visual Localization
Fahim, Masud; Söchting, Ilona; Ferranti, Luca; Kannala, Juho; Boutellier, Jani (2023-04-27)
Fahim, Masud
Söchting, Ilona
Ferranti, Luca
Kannala, Juho
Boutellier, Jani
Editori(t)
Gade, Rikke
Felsberg, Michael
Kämäräinen, Joni-Kristian
Springer
27.04.2023
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe20231012139903
https://urn.fi/URN:NBN:fi-fe20231012139903
Kuvaus
vertaisarvioitu
©2023 Springer. This is a post-peer-review, pre-copyedit version of an article published in Image Analysis: SCIA 2023. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-031-31435-3_6
©2023 Springer. This is a post-peer-review, pre-copyedit version of an article published in Image Analysis: SCIA 2023. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-031-31435-3_6
Tiivistelmä
Image based localization is a classical computer vision challenge, with several well-known datasets. Generally, datasets consist of a visual 3D database that captures the modeled scenery, as well as query images whose 3D pose is to be discovered. Usually the query images have been acquired with a camera that differs from the imaging hardware used to collect the 3D database; consequently, it is hard to acquire accurate ground truth poses between query images and the 3D database. As the accuracy of visual localization algorithms constantly improves, precise ground truth becomes increasingly important. This paper proposes TBPos, a novel large-scale visual dataset for image based positioning, which provides query images with fully accurate ground truth poses: both the database images and the query images have been derived from the same laser scanner data. In the experimental part of the paper, the proposed dataset is evaluated by means of an image-based localization pipeline.
Kokoelmat
- Artikkelit [2910]