利用太阳位置先验提升太空目标3D高斯点云的光照准确性
Improved 3D Gaussian Splatting of Unknown Spacecraft Structure Using Space Environment Illumination Knowledge
- 将太阳位置信息融入3D高斯点云训练，改善光照模拟
- 模型能适应太空快速变化的光照，准确呈现阴影与自遮挡
- 适合空间对接任务中需要高精度三维重建与相机位姿估计的场景
本文提出一种新流程，从空间交会与近距离操作（RPO）过程中拍摄的图像序列中恢复未知目标航天器的三维结构。目标的几何与外观以3D高斯点云（3DGS）模型表示。然而，3DGS训练依赖静态场景假设，与实际太空影像中动态光照条件相悖。训练后的3DGS模型还可通过光度优化用于相机位姿估计。因此，渲染图像的光度准确性对下游位姿估计至关重要。本文提出将服务航天器估算并维护的太阳位置先验知识引入训练流程，以提升3DGS光栅化的光度质量。实验表明，该方法使3DGS模型在图像序列训练中能适应太空快速变化的光照条件，准确反映全局阴影与自遮挡。
原文摘要 · Abstract (English)
This work presents a novel pipeline to recover the 3D structure of an unknown target spacecraft from a sequence of images captured during Rendezvous and Proximity Operations (RPO) in space. The target's geometry and appearance are represented as a 3D Gaussian Splatting (3DGS) model. However, learning 3DGS requires static scenes, an assumption in contrast to dynamic lighting conditions encountered in spaceborne imagery. The trained 3DGS model can also be used for camera pose estimation through photometric optimization. Therefore, in addition to recovering a geometrically accurate 3DGS model, the photometric accuracy of the rendered images is imperative to downstream pose estimation tasks during the RPO process. This work proposes to incorporate the prior knowledge of the Sun's position, estimated and maintained by the servicer spacecraft, into the training pipeline for improved photometric quality of 3DGS rasterization. Experimental studies demonstrate the effectiveness of the proposed solution, as 3DGS models trained on a sequence of images learn to adapt to rapidly changing illumination conditions in space and reflect global shadowing and self-occlusion.
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