Automatic occlusion removal from 3D maps for maritime situational awareness

TL;DR

This paper presents a deep learning-based method for removing occlusions from 3D maritime maps, improving their accuracy and visual fidelity without extensive reprocessing, thus enhancing situational awareness.

Contribution

It introduces a novel approach combining instance segmentation and generative inpainting to directly modify 3D meshes for occlusion removal in large-scale maritime environments.

Findings

Significant improvement in 3D model fidelity

Enhanced geometric and visual accuracy

Compatible with current geospatial standards

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.