IDU: Incremental Dynamic Update of Existing 3D Virtual Environments with New Imagery Data

TL;DR

The paper presents IDU, a pipeline that efficiently updates existing 3D virtual environments with new imagery by combining camera pose estimation, change detection, generative AI, and human guidance, reducing update time and costs.

Contribution

It introduces an incremental update method for 3D environments that integrates new data with minimal effort, enhancing military simulation maintenance.

Findings

Significantly reduces update time and labor costs.

Effectively integrates new objects into existing 3D models.

Maintains high accuracy with human-guided object placement.

Abstract

For simulation and training purposes, military organizations have made substantial investments in developing high-resolution 3D virtual environments through extensive imaging and 3D scanning. However, the dynamic nature of battlefield conditions-where objects may appear or vanish over time-makes frequent full-scale updates both time-consuming and costly. In response, we introduce the Incremental Dynamic Update (IDU) pipeline, which efficiently updates existing 3D reconstructions, such as 3D Gaussian Splatting (3DGS), with only a small set of newly acquired images. Our approach starts with camera pose estimation to align new images with the existing 3D model, followed by change detection to pinpoint modifications in the scene. A 3D generative AI model is then used to create high-quality 3D assets of the new elements, which are seamlessly integrated into the existing 3D model. The IDU pipeline incorporates human guidance to ensure high accuracy in object identification and placement, with each update focusing on a single new object at a time. Experimental results confirm that our proposed IDU pipeline significantly reduces update time and labor, offering a cost-effective and targeted solution for maintaining up-to-date 3D models in rapidly evolving military scenarios.