用新影像动态更新3D战场环境，省时省力。
IDU: Incremental Dynamic Update of Existing 3D Virtual Environments with New Imagery Data
- 通过相机位姿对齐与变化检测，定位场景新变化
- 仅需少量新图像即可完成单个物体的高质量重建
- 结合人工指导，适合军事仿真中快速迭代更新
为满足模拟与训练需求，军事机构已投入大量资源，通过大规模成像和3D扫描构建高分辨率3D虚拟环境。然而，战场条件动态变化——物体可能随时出现或消失——导致全规模更新既耗时又昂贵。为此，我们提出增量式动态更新（IDU）流程，仅需少量新获取的影像，即可高效更新现有3D重建模型，如3D高斯泼溅（3DGS）。该方法首先进行相机位姿估计，将新图像与现有3D模型对齐，随后通过变化检测识别场景中的变动区域。接着，利用3D生成式AI模型生成新元素的高质量3D资产，并无缝融合至原有模型中。IDU流程引入人工指导，确保对象识别与定位的高精度，每次更新聚焦于单一新增物体。实验结果表明，所提IDU流程显著降低更新时间和人力成本，为快速演化的军事场景提供一种高效、精准且低成本的3D模型维护方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。