In operational on-orbit observation missions, the proximity observation factors of the servicing spacecraft, including viewing sparsity, illumination conditions, and imaging quality, are the primary controllable elements that affect the performance of three-dimensional (3D) reconstruction. In this study, a dynamic model is integrated with the Blender rendering platform to design four types of proximity observation trajectories: co-moving flight, natural orbiting, V-bar maneuvering, and spiral approach. Various initial conditions, such as the target structure, orbital altitude, and observation distance, are configured to construct diverse observation scenarios and generate simulated image sequences. Subsequently, a parametric observation model based on 3D Gaussian Splatting is introduced to iteratively learn the implicit 3D representation of noncooperative space targets. Through adaptive density control, the number and spatial distribution of Gaussian primitives are dynamically adjusted, thus enabling the differentiable rendering of novel views and dense point clouds. Finally, by evaluating COLMAP convergence rates and image-quality metrics, the optimal combination of observation factors is identified, and the suitability of different orbital configurations for 3D reconstruction tasks is analyzed. The results indicate that image sequences acquired via V-bar maneuvering exhibit greater interframe parallax, achieving a COLMAP convergence rate of 91.16%. Moreover, the texture complexity of the target surface affects reconstruction quality. Additionally, the 3DGS algorithm effectively reconstructs complete and dense point clouds, achieving a Peak Signal-to-Noise Ratio of 39.81 dB on the test dataset of the Tango satellite.
Zhu et al. (Wed,) studied this question.