Automatic occlusion removal from 3D maps for maritime situational awareness

Felix Sattler; Borja Carrillo Perez; Maurice Stephan; Sarah Barnes

doi:10.1117/12.3030924

19 November 2024 Automatic occlusion removal from 3D maps for maritime situational awareness

Felix Sattler, Borja Carrillo Perez, Maurice Stephan, Sarah Barnes

Proceedings Volume 13196, Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX; 131960R (2024) https://doi.org/10.1117/12.3030924
Event: Remote Sensing, 2024, Edinburgh, United Kingdom

Abstract

We introduce a novel method for updating 3D geospatial models, specifically targeting occlusion removal in large-scale maritime environments. Traditional 3D reconstruction techniques often face problems with dynamic objects, like cars or vessels, that obscure the true environment, leading to inaccurate models or requiring extensive manual editing. Our approach leverages deep learning techniques, including instance segmentation and generative inpainting, to directly modify both the texture and geometry of 3D meshes without the need for costly reprocessing. By selectively targeting occluding objects and preserving static elements, the method enhances both geometric and visual accuracy. This approach not only preserves structural and textural details of map data but also maintains compatibility with current geospatial standards, ensuring robust performance across diverse datasets. The results demonstrate significant improvements in 3D model fidelity, making this method highly applicable for maritime situational awareness and the dynamic display of auxiliary information.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Felix Sattler, Borja Carrillo Perez, Maurice Stephan, and Sarah Barnes "Automatic occlusion removal from 3D maps for maritime situational awareness", Proc. SPIE 13196, Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX, 131960R (19 November 2024); https://doi.org/10.1117/12.3030924

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