Normalized localization for 6-DOF camera pose regression

Weiquan Huang; Yan Bai; Yixin Wang; Yutang Wu; Ming Feng; Yin Wang

doi:10.1117/12.2557660

3 January 2020 Normalized localization for 6-DOF camera pose regression

Weiquan Huang, Yan Bai, Yixin Wang, Yutang Wu, Ming Feng, Yin Wang

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 1137326 (2020) https://doi.org/10.1117/12.2557660
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

Visual localization determines the position of the viewer in a scene based on his or her viewpoint picture. The problem is challenging because we must handle various view-points in addition to picture quality. In this paper, we present a model that regresses the 6-DOF camera pose from a single RGB image. We use a spatial grid that splits the target space into cells and apply position regression after classifying the viewpoint into a cell. Combining with the adaptive loss and spatial LSTMs, our method outperforms existing approaches by a large margin in both indoor and outdoor scenarios. Furthermore, we present a new integrated indoor and outdoor localization dataset. Results on both public and our datasets show that our method can improve both positional and orientational precision, especially for large scenes.

Citation Download Citation

Weiquan Huang, Yan Bai, Yixin Wang, Yutang Wu, Ming Feng, and Yin Wang "Normalized localization for 6-DOF camera pose regression", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 1137326 (3 January 2020); https://doi.org/10.1117/12.2557660

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available