SpaceFast-GS: Foreground-Guided 3D Gaussian Splatting for Efficient Spacecraft Reconstruction

Multi-view optical reconstruction supports spacecraft inspection, target characterization, and analysis from viewpoints not observed during image acquisition. For time-sensitive space situational awareness, such reconstruction must balance fidelity with the time required to build and render the scene representation. Three-dimensional Gaussian splatting (3DGS) provides efficient novel-view rendering, but its general-purpose initialization and densification do not account for the strong spatial imbalance of spacecraft imagery, where a compact and structurally complex target is surrounded by a largely uninformative background. This mismatch can allocate computation to weakly supported regions, limit the reconstruction of thin structures and object boundaries, and enlarge the representation without a corresponding gain in fidelity. We propose SpaceFast-GS, a foreground-guided framework that introduces RGB-derived target evidence at three successive stages. Foreground-guided allocation (FGA) constructs relaxed multi-view support and places the initial Gaussian population directly in image-supported target regions, reducing the corrective growth required after generic initialization. Object- and boundary-aware refinement (OBR) retains full-image photometric supervision while increasing the contribution of spacecraft regions and silhouette transitions. Guided population control (GPC) combines optimization gradients with support confidence, thin-support evidence, and projected reconstruction residuals to prioritize densification toward a target population and avoid unnecessary primitive growth. All target evidence is obtained from the training RGB images and calibrated cameras, without external segmentation or pretrained reconstruction. On NASA3D Standard-29, SpaceFast-GS ranks second across five reconstruction measures and achieves 41.357±0.015 dB full-image PSNR in 159.4 s at 932.1 FPS. Relative to standard 3DGS, this represents a 32.3% reduction in training time and a 3.41-fold increase in rendering throughput; stage-wise ablations further verify the contribution of FGA, OBR, and GPC. These results show that coordinating Gaussian allocation, refinement, and population growth with foreground evidence can improve reconstruction turnaround and rendering efficiency while retaining competitive spacecraft reconstruction quality.