用物理仿真生成真实轨道光照下的卫星动态图像，评估3D重建算法性能。
Dynamic Scene 3D Reconstruction of an Uncooperative Resident Space Object
- 基于Isaac Sim构建真实轨道光照下的翻滚卫星仿真环境。
- Neuralangelo在静态场景下重建精度高，与原始CAD模型误差小。
- 为在轨服务与碎片清除任务提供高保真几何建模基础。
非合作空间目标（RSO）的表征在在轨服务（OOS）和主动碎片清除（ADR）任务中至关重要，用于评估其几何与运动特性。为应对翻滚非合作目标3D重建的挑战，本文评估了现有前沿3D重建算法在动态场景中的表现，重点关注其生成高保真几何模型的能力。为此，我们使用Isaac Sim构建了物理精确的仿真环境，生成了在真实轨道光照条件下翻滚卫星的2D图像序列。初步实验表明，采用Neuralangelo在静态场景下的重建质量优异：通过Cloud Compare对比，生成的3D网格与原始CAD模型匹配度高，误差与伪影极小，能够捕捉任务规划所需的关键细部特征。该结果为后续动态场景重建评估提供了基准。
原文摘要 · Abstract (English)
Characterization of uncooperative Resident Space Objects (RSO) play a crucial role in On-Orbit Servicing (OOS) and Active Debris Removal (ADR) missions to assess the geometry and motion properties. To address the challenges of reconstructing tumbling uncooperative targets, this study evaluates the performance of existing state-of-the-art 3D reconstruction algorithms for dynamic scenes, focusing on their ability to generate geometrically accurate models with high-fidelity. To support our evaluation, we developed a simulation environment using Isaac Sim to generate physics-accurate 2D image sequences of tumbling satellite under realistic orbital lighting conditions. Our preliminary results on static scenes using Neuralangelo demonstrate promising reconstruction quality. The generated 3D meshes closely match the original CAD models with minimal errors and artifacts when compared using Cloud Compare (CC). The reconstructed models were able to capture critical fine details for mission planning. This provides a baseline for our ongoing evaluation of dynamic scene reconstruction.
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